production and operations management poms · pdf fileand production and operations management...

Operations in Financial ServicesmdashAn Overview

Emmanuel D (Manos) HatzakisGoldman Sachs Asset Management Goldman Sachs amp Co New York New York 10282-2198 USA manoshatzakisgscom

Suresh K NairDepartment of Operations and Information Management School of Business University of Connecticut Storrs Connecticut 06269-1041 USA

sureshnairbusinessuconnedu

Michael L PinedoDepartment of Information Operations and Management Science Leonard N Stern School of Business New York University

New York New York 10012-1106 USA mpinedosternnyuedu

We provide an overview of the state of the art in research on operations in financial services We start by highlighting anumber of specific operational features that differentiate financial services from other service industries and discuss

how these features affect the modeling of financial services We then consider in more detail the various different researchareas in financial services namely systems design performance analysis and productivity forecasting inventory and cashmanagement waiting line analysis for capacity planning personnel scheduling operational risk management and pricingand revenue management In the last section we describe the most promising research directions for the near future

Key words financial services banking asset management processes operationsHistory Received November 2009 Accepted July 2010 by Kalyan Singhal after 2 revisions

1 IntroductionOver the past two decades research in service oper-ations has gained a significant amount of attentionSpecial issues of Production and Operations Managementhave focused on services in general (Apte et al 2008)and various researchers have presented unified theories(Sampson and Froehle 2006) research agendas (Roth andMenor 2003) literature surveys (Smith et al 2007)strategy ideas (Voss et al 2008) and have discussedthe merits of studying service science as a new discipline(Spohrer and Maglio 2008) A few books and a specialissue of Management Science have focused on the oper-ational issues in financial services in particular (seeHarker and Zenios 1999 2000 Melnick et al 2000)However financial services have still been given scantattention in much of the literature relative to otherservice industries such as transportation health careentertainment and hospitality The dilution of focus byconcentrating on more general distinguishing featuresdoes not do justice to financial services where some ofthese characteristics are not central (The more generalfeatures that are typically being considered includeintangibility heterogeneity contemporaneous produc-tion and consumption perishability of capacity waitinglines (rather than inventories) and customer participa-tion in the service delivery)

In this overview we mean by financial servicesprimarily firms in retail banking commercial lending

insurance (other than health) credit cards mortgagebanking brokerage investment advisory and assetmanagement (mutual funds hedge funds etc)

11 Importance of Financial ServicesFinancial services firms are an important part of theservice sector in an economy that has been growingrapidly over the past few decades These firms pri-marily deal with originating or facilitating financialtransactions The transactions include creation liqui-dation transfer of ownership and servicing ormanagement of financial assets they could involveraising funds by taking deposits or issuing securitiesmaking loans keeping assets in custody or trust ormanaging them to generate return pooling of risk byunderwriting insurance and annuities or providingspecialized services to facilitate these transactions

Services is a large category that encompasses firmsas diverse as retail establishments transportationfirms educational institutions consulting informa-tion legal taxation and other professional real estateand healthcare Even within financial services there isa wide variety of firms which are characterized byunique production processes and specialized skillsThe processes and skills required for banking arequite distinct from solicitations for credit cards ac-quisitions of new insurance accounts or the handlingof equity dividends and proxy voting for example

633

PRODUCTION AND OPERATIONS MANAGEMENTVol 19 No 6 NovemberndashDecember 2010 pp 633ndash664ISSN 1059-1478|EISSN 1937-5956|10|1906|0633

POMSDOI 103401poms108001163

r 2010 Production and Operations Management Society

Even though services account for about 84 of thetotal employment in the economy only about 4 ofthis workforce is employed in financial services Thismight come as a surprise to some because financialservices transactions in one form or another are soubiquitous in our lives Not surprisingly however thenumber of financial services firms is about 7 of thetotal non-farm firms and contributes about 13 of to-tal non-farm sales Only wholesale trade has a similaremployment and number of firms with a larger con-tribution to sales

Table 1 provides employment information for thesub-codes within financial services As can be seenretail banks insurance companies and insurance bro-kers together employ about two-thirds of the financialservices workforce A cursory look at the table gives asense of the diversity of the services sector Clearlyoperations management problems and approachesused to solve them have to be customized for partic-ular types of servicesmdashwe already know that whatworks for manufacturing may not work for servicesbut by looking at Table 1 we can also realize that whatworks for retail trade or recreational services may notwork for financial services A quick glance throughMonstercomrsquos job openings for operations managersin financial service firms shows a wide variety of ti-tles responsibilities and lsquolsquoproductsrsquorsquo related to suchjobs This is shown in Table 2 and gives a sense of thewide swath of topics that could be covered in aca-demic research on financial services operations Asfinancial services are such an important segment of

the services economy we wish to explore whetheroperations in financial services are indeed unique orshare several characteristics with services in generalThat is the motivation for this special issue

12 Distinctive Characteristics of Operations inFinancial ServicesThere are several unique operational characteristicsthat are specific to the financial services industry andthat have not been given sufficient attention in thegeneral treatment of services in the extant literatureWe list below a number of these unique operationalcharacteristics and elaborate on them in what follows

Fungible products with an extensive use oftechnology

High volumes and heterogeneity of clients Repeated service encounters Long-term contractual relationships between

customers and firms Customersrsquo sense of well-being closely inter-

twined with services Use of intermediaries Convergence of operations finance and marketing

121 Fungible Products with an Extensive Useof Technology One obvious difference betweenoperations in financial services and operations inmanufacturing and in other service industries is thatthe lsquolsquowidgetsrsquorsquo in financial services are money or relatedfinancial instruments As there is a declining use of thephysical vestiges of money such as coins currency

Table 1 Employment within Financial Services

NAICS code Title within financial services

Total employees

Number

5211 Monetary authorities-central bank

(Federal Reserve banks etc)

21510 04

5221 Depository credit intermediation

(banks credit unions etc)

1816300 307

5222 Non-depository credit intermediation

(credit cards mortgage lending etc)

659930 112

5223 Activities related to credit intermediation

(brokers for lending)

294910 50

5231 Securities and commodity contracts

intermediation and brokerage

516010 87

5232 Securities and commodity exchanges 8010 01

5239 Other financial investment activities

(mutual funds etc)

344950 58

5241 Insurance carriers 1258050 213

5242 Agencies brokerages and other

insurance-related activities

907880 153

5251 Insurance and employee benefit funds 47730 08

5259 Other investment pools and funds 41190 07

5916470 1000

Source Bureau of Labor Statistics statblsgovoeshomehtm May 2008

Table 2 A sample of Financial Service Operations Job Titles Responsi-bilities and Products

Titles Products

Vice President Operations Opera-

tions Manager Financial Operations

Supervisor Foreclosure and Bank-

ruptcy Operations Manager Risk

Operations Team Manager Team

Manager Ops Control-Fixed Income

National Director Operations Hedge

Fund Operations Specialist VP

Director Of Operations

Premiums Claims Refunds Cash

flow and treasury management

Customer statements Loan servi-

cing and support Trade

confirmations Reconciliations Tax

reporting Security settlements

Mortgages

Nature of workresponsibility

Brokerage operations Improve customer service resolves customer issues

Review security pricing Vendor support Authorize net settlement Hand-off of

data ensure data integrity Verifying transactions Tracking missing transactions

Leverage technology Maintain ops controls update policies procedures Back-

office support Understand regulations Ensure compliance Attain profit and

revenue benchmarks Reduce risk Improve quality Six sigma Operational

processing efficiency Problem solving Ensure best practices Streamline

activities Manage key expenses Work management tools Monitors work flow

Productiontesting

Source Monstercom jobs listings during the week of March 8 2009

Hatzakis Nair and Pinedo Operations in Financial ServicesmdashAn Overview634 Production and Operations Management 19(6) pp 633ndash664 r 2010 Production and Operations Management Society

bond or stock certificates much of the transactions arein the form of bits and bytes Thus inventory is fungibleand can be transported broken up and reconstituted(facilitating securitization eg) in malleable ways thatare simply not possible in manufacturing or in otherservice industries (see eg the recharacterization ofbank reserves in Nair and Anderson 2008)

The increased use of online transactions (in broker-ages credit card payments retail banking and retire-ment accounts eg) are forcing fundamental changes inthe way operations managers think about capacity is-sues (for statement mailing and remittance processingor for transfer of ownership in securities eg) The factthat adoption of online transactions is still growing andhas not yet matured and leveled off makes capacityplanning a big challenge Yet we are aware of very littleresearch that would help managers deal with this issue

122 High Volumes and Heterogeneity of ClientsFinancial services are characterized by very highvolumes of customers and transactions Furthermorecustomers are not all alike In many firms a smallfraction of the customers generate most of the profitsgiving the firms an incentive to view them differentlyand provide differential treatment given the firmsrsquolimited resources For example high net worthindividuals may be treated differently by assetmanagement firms banking clients who keep highbalances in checking accounts and transact heavily maybe handled differently from depositors who keep almostall their funds in savings accounts and certificates ofdeposits (CDs) and transact minimally revolvers (iecustomers who carry over balances from one month tothe next) may be regarded differently from transactors(customers who do not revolve balances) by a creditcard firm In most non-financial services because ofa limited number and sporadic interactions withcustomers (eg in restaurants and amusement parks)one customer is considered for the most part similar tothe next one in terms of margins and attention required

123 Repeated Service Encounters In contrast toother service industries where research typicallyfocuses on a single encounter (lsquolsquothe moment oftruthrsquorsquo lsquolsquowhen the rubber hits the roadrsquorsquo) financialservices are characterized by repeated serviceencounters or potential encounters between the firmand its customers due to regular monthly statementsyear-end statements buysell transactions insuranceclaims money transfers etc Anecdotal evidence fromthe brokerage and investment advisory industrysuggests that clients with low asset balances andtransaction volumes contribute the least to firm revenueand the most to operational cost through calls forcustomer service One online bank discouraged calls tocustomer service because it found that just a few calls by

a client could wipe out all the profit from the clientrsquossavings account Very little research in service operationsmanagement has focused on this issue New customersconstitute another major group that is more likely tomake calls with billing questions or inquiries regardingtheir statements Should billing and statementing to newcustomers be handled differently perhaps with morecare than to existing customers Obviously if thisobservation is true differential handling can reduce thetraffic to call centers Existing call center research usuallyassumes the call volume to be a given for the most partand the focus is on lsquolsquomanagingrsquorsquo the traffic This is akin totraditional manufacturing where it was assumed thatlarge setup times were a given and a good way tolsquolsquomanagersquorsquo would be to use an optimal batch size Onelearns to live with such a constraint Not until just intime (JIT) manufacturing came along did managersquestion why setup times were large and what could bedone to reduce them

At one credit card company operations managersstruggled for years to cope with volatile demand inbill printing mailing remittance processing and callcenter operations Daily volumes could fluctuateeasily between half a million and one and a halfmillion pieces of mail in remittance processing andmanagers were reconciled to high overtimes and idletimes because they felt they had no control overwhen customers mailed in their checks and reduc-ing float was important Call volumes at the callcenters were similarly volatile as were volumes inthe bill printing and mailing operations This situa-tion continued until someone recognized that allthese problems were interconnected and to a largeextent within the control of the firm As it happensthe portfolio of current customers is distributed intoabout 25 cycles one for each working day of themonth For example customers in the 17th cycle arebilled on the 17th working day of the month Care istaken to ensure that customers in the same zip codeare put in the same cycle so volume-mailing dis-counts from the US postal service can be obtainedand the cycles are level loaded However this allo-cation to cycles was carried out several years backand over time some customers had closed theiraccounts while new customers had been added toexisting cycles resulting in large differences inthe numbers of customers in the various cyclesand in a wide variability in the printing and mailingof monthly statements On the remittance side ananalysis found that there was less randomness incustomer payment behavior than one would expectThere were broadly four cohorts of customersone that sent in payments on receipt of the state-ment a second that mailed checks based on duedate a third that acted based on salary paymentdate and a fourth that acted randomly The first two

Hatzakis Nair and Pinedo Operations in Financial ServicesmdashAn OverviewProduction and Operations Management 19(6) pp 633ndash664 r 2010 Production and Operations Management Society 635

cohorts the largest ones were in fact dependent onthe cycles that the firm had set up many years backSimilarly billing calls were also heaviest soon afterthe statement was received by the customer againtraffic that was determined by the cycles created bythe firm

If the cycles could now be level loaded many ofthese problems would disappear (similar to whathappened in manufacturing when setup times weredramatically reduced thereby enabling lean opera-tions) But there was a problemmdashthe firm needed toinform each customer if their cycles were moved forgood reason because customers needed to plan theirfinances However this notification was not neces-sary if the move was to be within 3 days fromtheir current cycle An optimization model foundthat this constrained move was sufficient to take careof the vast majority of moves that were initiallythought to be necessary

This example illustrates how stepping back andtaking a broader view of the situation and collabo-rating across processes can have a major impact onfinancial service operations something that is lack-ing in the current literature

124 Long-Term Contractual Relationships BetweenCustomers and Firms Connected to the previouscharacteristic of repeat encounters is the recognitionthat unlike in other services in financial services thefirm and the customer have a relatively long-termcontractual arrangement However technology and in-formation availability makes comparison shoppingeasy resulting in easy switching between firms andtherefore high attrition This loss of customers makes theacquisition process very important to the continuedgrowth and profitability of the firm Similarly loyaltyprograms (such as rewards and balance transferprograms in the credit card business) are important tostanch the bleeding The design and execution of theseprograms are based on complicated processes that needto consider risks costs redemptions incremental salesscheduling and sequencing of offers etc Researchers infinancial services operations by not making theirpresence felt in these areas are missing the boat withregard to issues that are the most important (lsquolsquomust dorsquorsquoactivities) for the firm and may be paying instead toomuch attention to relatively mundane and low-impactissues (lsquolsquogood to dorsquorsquo activities)

Just as the above processes aim at increasing rev-enue there may be other processes that are put inplace to reduce unnecessary costs In the insurancebusiness for example the claims processes may pri-marily revolve around a call center which hasattracted sufficient attention in the literature as wewill see later But unnecessary costs can be reducedby fraud prevention and detection and subrogation

activities (money the firm pays out but is owedto it by other carriers) Timely intervention canavoid expiry of opportunities to collect dollars owedand more attention could be paid to even smallopportunities There is an extensive literature in riskand insurance journals on scoring for fraud pre-vention and detection but leveraging that informa-tion in the claims process can benefit from anoperations perspective

Another example from the insurance industryconcerns workerrsquos compensation claims where theprocess for handling workplace injury can havelong tails spanning several years before the claimis closed The process is complicated with inter-actions between the worker the employer medicalpractitioners hospitals state authorities and law-yers (both on the staff of the insurance firm andpanel counsel ie lawyers who are hired on anad hoc basis) There are several opportunitieshere (Jewell 1974) to speed up the process (andspeed up the workersrsquo return to work which is inthe insurance firmrsquos interest) reduce costs detectfraud ensure that review triggers are not over-looked increase the utilization of staff counselcompared with panel counsel by better schedulingof appearances for hearings etc We are unawareof any recent operations management literature inthis area

125 Customersrsquo Sense of Well-Being CloselyIntertwined with Services Along with the ease ofmanipulating the putty at the core of the financialservices process comes the responsibility of workingwith something that is so close to the customerrsquos senseof well-being and worth Poor operations manage-ment that results in delays quality issues or sloppi-ness can and will attract regulatory scrutiny andunfavorable publicity and will generate immediaterebukes from the customer in the form of callscomplaints and because the account can be easilymoved around customer attrition At least two factorsmake the detection of errors due to operational faultsand their exposure to the clients relatively easy infinancial services

(i) the amount frequency and detail of communi-cation and disclosure as required by regulationand

(ii) the clientsrsquo heightened propensity and incentiveto check for error in something so closely linkedto their livelihood and sense of security

Because of the above tolerance for error is signifi-cantly lower than in other industries For examplefaulty processes resulting in incorrect calculations ofinterest amounts in savings mortgage loan or creditcard accounts or in inappropriate handling of stock


dividend payments become obvious immediatelyafter monthly statements are sent out

The above customer service issues are distinctfrom the perceived quality of the performance of in-dividual customer brokerage portfolios retirementaccounts annuities mutual funds and interest accruedin retail banking With the plethora of informationavailable comparing a customerrsquos firm with others inthe same space moving an account to the competitionis only a few clicks away Even though performancemay depend on the economy the stock market invest-ment research and fund manager performancerecognizing the costs (Schneider 2010) and capacityissues (the increased transactions during market tur-bulence eg) are important operations managementconcerns that have received little attention

126 Use of Intermediaries This is an importantaspect of the financial services industry In some casesa direct-to-consumer approach is used (credit cards)in other cases most of the customer facing work isdone by intermediaries (financial advisors insuranceagents annuity sales through banks etc) and in stillother cases the firmrsquos employees and its agents haveto collaborate with one another (insurance) Workingthrough an intermediary entails a set of issues notnormally seen in other services that function withoutintermediaries For example financial product andservice design and delivery get filtered through theprism of what the agent feels is in his or her own bestinterest At times the relationship between the firmand the intermediary is not exclusive hereby adding alayer of complexity because the customer may choosebetween products from competing firms Thereforewhat gets planned in the corporate offices of thefinancial services firms and what is seen by thecustomer may be quite different The operationsmanagement literature to our knowledge has notpaid attention to product and service design in suchsituations because the implications on customerlifetime interactions with the firm go much beyondinitial pricing product features and the inventory ofbrochures left with the agents

127 Convergence of Operations Finance andMarketing There is probably no other industry wherethis convergence is more pronounced These functionsare supported and enabled by a healthy dose ofstatistics technology and optimization By focusingonly on back-office operations such as call centersresearchers in service operations are leaving a lot on thetable There is very little research in the serviceoperations literature that leverages this convergencewhich requires a choreography as described by Vosset al (2008) who put it in a more limited context ofoperations and marketing For example the client

acquisition process in full-service retail brokerage andinvestment advisory firms begins at the corporate levelwhere it draws resources from marketing strategyinformation technology and operations and is ulti-mately implemented through the sales force of brokersfinancial advisors Customer acquisition at a credit cardcompany is a competitive differentiator and a complexprocess focused on direct mail campaigns At manylarge firms the budgets for direct mail run into severalhundred million dollars annually By focusing on thebilling mailroom collections call centers and billingcall centers researchers in service operations are work-ing on a problem akin to quality inspectors at the end ofthe production linemdashby then it is too late the volumesof mail and calls are baked in during the mailingcampaign creation while their skills could have madethe mailings more effective and targeted (given theminuscule response rates) resulting in fewer delin-quent accounts (requiring fewer outbound collectioncalls) and perhaps also fewer billing calls to inboundcall centers

At a more sophisticated level very few credit cardfirms use contact history in mailing solicitationswhich may result in repeated mailings to chronicnon-responders The managers developing campaignstrategies may not have the analytical background thata researcher in service operations can bring to bearand the cycle time for campaign creation is typicallyso long and complicated that much attention getsexpended on scoring for credit and response filetransfers from credit bureaus and data vendors scrub-bing of data etc These complicated processes leavelittle time to incorporate experience from a previousmailing because a reading of the results of that mailingtakes time (prospective customers may not respondimmediately even if they do respond) and file struc-tures may not have been designed to carry informationabout previous contacts and the response to them

Another indication of this convergence is that themajority of the undergraduate and MBA hiring at aninvestment bank in the greater New York City areafrom one of the regionrsquos business schools is in theCOOrsquos operation whether the student concentra-tions are in finance marketing information systemsor operations

The foregoing does not imply that no significantwork has been done in the research on financialservice operations just that areas of work have had anarrow focus The purpose of this article and thisspecial issue is to begin to expand that focus andencourage research in neglected or emerging areas infinancial service operations We will survey existingresearch next not only where the attention is only onfinancial service operations but also where researchin service industries in general has substantial appli-cation in financial service operations In Appendix


A we provide an overview of the various operationsprocesses in financial services and highlight theones that have been addressed in operations man-agement literature

This survey paper is organized as follows Sections2 through 9 go over eight research directions that areof interest from an operations management point ofview The first couple of sections consider the moregeneral research topics whereas the later sections gointo more specific topics and more narrowly orientedresearch areas Section 2 focuses on process and sys-tem design in financial services while section 3considers performance measurements and analysisSection 4 deals with forecasting because forecastingplays a major role in virtually every segment of thefinancial services industry The next section focuseson cash and liquidity management this section re-lates cash management to classic inventory theorySections 6 and 7 deal with waiting line managementand personnel scheduling in retail banking and incall centers Even though these two topics are stronglydependent on one another they are treated separatelythe reason being that the techniques required for deal-ing with each one of these two topics happen to bequite different from one another Section 8 focuseson operational risk in financial services This areahas become very important over the last decade andthis section describes how this area relates to otherresearch areas in operations management such as to-tal quality management (TQM) Section 9 considersproduct pricing and revenue management issues Thelast section section 10 presents our conclusions anddiscusses future research directions

2 Financial Services System DesignService systems design has attracted quite a bit ofattention in the academic literature It is clear thatservice design has to be as rigorous an activity asproduct design because the customer experiences theservice first hand much like a product and comesaway with impressions regarding the quality of ser-vice Although the quality of service delivery dependson a number of factors such as associate trainingtechnology traffic neighborhood customer profileaccess to the service (channel access) and quality ofresource inputs the service experience gets baked intothe process at the time of the service design itself andtherefore a proper service design is fundamental tothe success of the customer experience

21 Aspects of Service DesignService research has usually focused on capacity man-agement (type of customer contact scheduling anddeployment) and the impact of the response to vari-ability on costs and quality For long the nature ofcustomer contact has influenced service design think-

ing by creating front-officeback-office functions(Sampson and Froehle 2006 Shostack 1984) Shostackalso pioneered the use of service blueprinting foridentifying fail points where the firm may face qualityproblems She illustrated this methodology for a dis-count brokerage and correctly identified that many ofthe operational processes are not seen by the customershe then focused on the telephone communication stepthe only one with client contact This focus on clientcontact tasks whether in the front office or in the backoffice is widespread in services research in general andin research on financial services operations in particularOne reason may be that service researchers have foundit necessary to motivate their work by differentiatingservices from products (whether it is service marketingvs product marketing or service design vs productdesign) and client contact is an obvious differentiator

From the outset it has been clear that serviceprocesses are subject to a significant amount of ran-domness from various sources Frei (2006) discussesthe various sources of randomness in service processesand how firms react to them in the design of theirservices She identifies five types of variabilitymdashcustomer arrival variability request variability cus-tomer capability variability customer effort variabilityand customer preference variability She states thatfirms design services to factor in this variability bytrying either to accommodate the variability at a highercost (cross training of employees increased automa-tion variable staffing) or to reduce the variability witha view to increasing efficiency rather than cost (offpeak pricing standard option packages combo meals)

22 Focus on Single EncountersMuch of the services literature however focuses onsingle service encounters which are common in ser-vices such as fast food Even if a customer repeatedlyvisits the same restaurant there is not the kind ofstickiness to the relationship as can be found infinancial services Retail banking seems to haveattracted the most attention among financial serviceswith respect to service design but here again thefocus is on disparate single visits to the branch orAutomated Teller Machine (ATM) rather than as partof a life cycle of firmndashcustomer interactions Otherthan meeting the branch manager when opening anaccount there is usually no other recognition of thestage of the relationship in the delivery of servicePerhaps this will change with time as more firms startexperimenting with their service delivery design asBank of America has been doing (Thomke 2003)

23 Descriptive vs Prescriptive Studies of FinancialServicesSeveral descriptive studies have focused on retailbanking (Menor and Roth 2008 Menor et al 2001)


substitution of labor with information technology(Fung 2008) the use of customer feedback to improvecustomer satisfaction (Krishnan et al 1999) the useof distribution channels (Lee et al 2004 Xue et al2007) self-service technologies (such as ATMspay at the pump see Campbell and Frei 2010a bMeuter et al 2000) online banking (Hitt andFrei 2002) and e-services in general (see Boyer et al2002 Ciciretti et al 2009 Clemons et al 2002 Furstet al 2002 Menor et al 2001) These studies talk aboutthe types of customers who use the various differentchannels and how firms have diversified their deliv-ery of services using these new channels as newertechnologies have become available However theyare usually descriptive rather than prescriptive inthat they speak about how existing firms and cus-tomers have already adopted these technologiesrather than what they should be doing in the futureFor example there are few quantitative metrics tomeasure a product (eg its complexity vis-a-viscustomer knowledge) a process (eg face to face vsautomated) and proximity (on-site or off-site) to helpa manager navigate financial service operations strat-egies from a design standpoint based on where herfirm is now In that sense financial service systemdesign still has ways to go to catch up with productdesign (product attributes customer utility pricingform and function configuration product develop-ment teams etc) and manufacturing process design(process selection batchline capacity planningrigidflexible automation scheduling location analy-sis etc) Because batching and lot sizing issues havebeen of considerable interest in the history of thestudy of manufacturing processes and because onlinetechnologies have made the concept of batching con-siderably less important it would be interesting to seehow research in service systems design unfolds in thefuture One paper with prescriptive recommendationsfor service design in the property casualty insuranceindustry is due to Giloni et al (2003)

3 Financial Services PerformanceMeasurement and Analysis

31 Best Practices and Process ImprovementMany service firms are measuring success by factorsother than profitability using such factors as customerand employee loyalty as measured by retentiondepth of relationship and lifetime value (Heskettet al 1994) Chen and Hitt (2002) in an empiricalstudy on retention in the online brokerage industryfound that ease of use breadth of offerings and qual-ity reduce customer attrition Balasubramanian et al(2003) find that trust is important for online transac-tions because physical appearance of branches etcno longer matter in such situations Instead perceived

environmental security operational competence andquality of service help create trust

In general service quality is difficult to manage andmeasure because of the variability in customer expec-tations their involvement in the delivery of theservice etc In general there may be two differentmeasures of service quality that are commonly usedthe first refers to and measures the actual service pro-vided (eg customer satisfaction resolution etc) thesecond may refer to the availability of service capac-itypersonnel (eg service level availability waitingtime etc) The first type of quality measure is not asnebulous in financial services where the output isgenerally related to monetary outcomes If there is anerror in the posting of a transaction or if quarterlyreturns from a mutual fund are below industry per-formance there is an immediate customer reactionand the points in the service design that caused suchfailures to occur is apparent whether it is in remit-tance processing or in the hiring of a fund managerQuality in financial services is not influenced by suchmatters as the mood of the customer as may be thecase in other services This makes ensuring quality infinancial services more doable and one of the foci ofthe research in operational risk management whichwe will discuss later

Roth and Jackson (1995) found that market intelli-gence and imitation of best practices can be aneffective way of improving service quality and thatservice quality is more influenced by service processchoices and the cumulative impact of investmentsthan by peoplersquos capabilities Productivity measure-ment in services is also a challenge (Sampson andFroehle 2006) Bank performance as a result of processvariation has been studied by Frei et al (1999)

This current special issue of Production and Opera-tions Management provides some interesting newcases of process improvement in financial servicesThe paper by Apte et al (2010) lsquolsquoAnalysis andimprovement of information-intensive services Evi-dence from insurance claims handling operationsrsquorsquopresents a classification of information-intensiveservices based on their operational characteristicsthis paper proposes an empirically grounded concep-tual analysis and prescriptive frameworks that can beused to improve the performance of information- andcustomer contact-intensive services The paper by DeAlmeida Filho et al (2010) focuses on collection pro-cesses in consumer credit They develop a dynamicprogramming model to optimize the collections pro-cess in consumer credit Collection processes havebeen the Cinderella of consumer lending researchbecause psychologically lenders do not enjoy analyz-ing their mistakes and also once an accounting loss isascribed to a defaulted loan there had been littleincentive for senior managers to keep track of how


much will be subsequently collected The paper byBuell et al (2010) investigates why self-servicecustomers are more reluctant to change their serviceprovider This paperrsquos primary contribution is toinvestigate how satisfaction and switching costs con-tribute to retention among self-service customers Thisis a particularly important issue in the financial ser-vices industry where considerable investments havebeen made in developing self-service distributionchannels and migrating customers to them

32 An Example of Best Practices AssetManagementAsset management provides an interesting example ofan area within the financial services sector that hasbeen receiving an increasing amount of researchattention with regard to best practices from variousoperations management perspectives The body ofresearch on operations management in asset manage-ment is growing however not always produced byoperations management researchers but often bythose in the finance world (Black 2007 Brown et al2009a b Kundro and Feffer 2003 2004 Stulz 2007)who examine operational risk issues in hedge fundsA collection of operations management researchpapers in asset management can be found in a recentbook by Pinedo (2010) Alptuna et al (2010) present abest practices framework for the operational infra-structure and controls in asset management and arguethat it is possible to effectively implement such aframework in organizations that enjoy a strong prin-ciple-based governance They examine conditionsunder which the cost-effective strategy of outsourc-ing asset management operations can be successfulfor asset managers and their clients Figure 1 whichhas been adapted from Alptuna et al (2010) shows

the multiple constituent parts that must work togetherin order for a typical asset management organizationto function effectively Figure 2 also adapted fromAlptuna et al (2010) lists the functions in the invest-ment management process according to their distancefrom the end client Typically the operations-intensivefunctions reside in the middle and back officesaccordingly the untapped research potential of oper-ations in asset management must be sought there Onecan create a similar framework as shown in Figures 1and 2 for a typical retail bank credit card issuermortgage lender brokerage trust bank asset custo-dian life or propertycasualty insurer among othersnone of which is less complex than an asset managerOutsourcing operations adds to the complexity by in-troducing elements of quality control for outsourcedpieces and coordination between the main organiza-tion and the third-party provider (State Street 2009)To develop their framework Alptuna et al (2010)draw heavily on asset management industry resourceson best practices namely the Managed Funds Associ-ationrsquos Sound Practices for Hedge Fund Managers(2009) the Report of the Asset Managersrsquo Committee tothe Presidentrsquos Working Group on Financial Markets(2009) the Alternative Investment Management Asso-ciationrsquos Guide to Sound Practices for European HedgeFund Managers (2007) and the CFA Institutersquos AssetManager Code of Professional Conduct (2009)

Schneider (2010) provides a framework for assetmanagement firms to analyze their costs Arfelt (2010)proposes an adaptation of the Lean Six Sigma frame-work used in automobile manufacturing for assetmanagement Biggs (2010) advocates a decentraliza-tion of risk management accountability as well astechnology and expense control in asset managementfirms Cruz (2010) argues that the focus of cost man-

Asset managemento Investment research management

and execution o

o

o

Sales and client relationship managementProduct development

Marketing

Independent internal oversight functions

o Compliance legal and regulatory o Controllers o Credit and market risk

management o Internal audit o Valuation oversight

Internal support teamso Billing o Human resources o Operations o Operational risk o Performance o Tax o Technology o Treasury

External service providerso Brokerage clearing and execution o Custody and trust services o Fund administrator o Prime brokerage and financing o Reputable auditor o Valuation (reputable third-party

valuation firm)

Figure 1 Typical Structure of an Asset Management Organization


agement programs at asset management firms shouldbe strategic and tactical (see also Cruz and Pinedo2009) Nordgard and Falkenberg (2010) give an ITperspective on costs in asset management Campbelland Frei (2010a) examine cost structure patternsin the asset management industry Amihud andMendelson (2010) examine the effect of transactioncosts on asset management and study their implica-tions for portfolio construction fund design tradeimplementation cash and liquidity management andcustomer acquisition and development strategies

33 Performance Analysis Through DataEnvelopment Analysis (DEA)There are numerous studies on performance and pro-ductivity analyses of retail banking that are based onDEA DEA is a technique for evaluating productivitymeasures that can be applied to service industries ingeneral It compares productivity measures of differ-ent entities (eg bank branches) within the sameservice organization (eg a large retail bank) to oneanother Such a comparative analysis then boils downto the formulation of a fractional linear program DEAhas been used in many retail banks to compareproductivity measures of the various branches withone another Sherman and Gold (1985) Sherman andLadino (1995) and Seiford and Zhu (1999) performedsuch studies for US banks Oral and Yolalan (1990)performed such a study for a bank in Turkey Vassi-loglou and Giokas (1990) Soteriou and Zenios (1999a)

Zenios et al (1999) Soteriou and Zenios (1999b) andAthanassopoulos and Giokas (2000) for Greek banksKantor and Maital (1999) for a large Mideast bankand Berger and Humphrey (1997) for various inter-national financial services firms These papers discussoperational efficiency profitability quality stock mar-ket performance and the development of better costestimates for banking products via DEA Cumminset al (1999) use DEA to explore the impact oforganizational form on firm performance They com-pare mutual and stock property liability companiesand find that in using managerial discretion and cost-efficiency stock companies perform better and in linesof insurance with long payouts mutual companiesperform better

Cook and Seiford (2009) present an excellent over-view of the DEA developments over the past 30 yearsand Cooper et al (2007) provide a comprehensivetextbook on the subject For a good survey andcautionary notes on the pitfalls of improper interpre-tation and use of DEA results (eg loosely using theresults for evaluative purposes when uncontrollablevariables exist) see Metters et al (1999) Zhu (2003)discusses methods to solve imprecise DEA (IDEA)where data on inputs and outputs are either boundedordinal or ratio bounded where the original linearprogramming DEA formulation can no longer be used

Koetter (2006) discusses the stochastic frontieranalysis (SFA) as another bank efficiency analysisframework which contrasts to the deterministic DEA

Asset management - Investment research - Portfolio and risk

management -

-

Sales and client relationshipmanagementProduct development

Trade execution - Financial

InformationeXchange (FIX) connectivity

- Trade order management and execution

Middleoffice

Investment operations - Billing - Cash administration - Client data warehouse - Client reporting

- Corporate actions processing

- Data management - OTC derivatives

processing

- Performance and analytics

- Portfolio recordkeepingand accounting

- Reconciliation processing

- Transaction management

Back office Fund accounting - Daily monthly and ad-

hoc reporting - General ledger - NAV calculation - Reconciliation - Security pricing

Global custody - Assets safekeeping - Cash availability - Failed trade

reporting- Incometax

reclaims- Reconciliation - Trade settlement

Transfer agency - Shareholder

servicing

Frontoffice

Figure 2 Investment Management Process Functions


4 ForecastingForecasting is very important in many areas of thefinancial services industry In its most familiar form inwhich it presents itself to customers and the generalpublic it consists of economic and market forecastsdeveloped by research and strategy groups in broker-age and investment management firms However thetypes of forecasting we discuss tend to be more inter-nal to the firms and not visible from the outside

41 Forecasting in the Management of CashDeposits and Credit LinesDeposit-taking institutions (eg commercial bankssavings and loan associations and credit unions) areinterested in forecasting the future growth of theirdeposits They use this information in the process ofdetermining the value and pricing of their depositproducts (eg checking savings and money marketaccounts and also CDs) for assetndashliability manage-ment and for capacity considerations Of specialinterest to these institutions are demand depositsmore broadly defined as non-maturity depositsDemand deposits have no stated maturity and thedepositor can add to the balance without restrictionor withdraw from lsquolsquoon demandrsquorsquo ie without warningor penalty In contrast time deposits also known asCDs have a set maturity and an amount establishedat inception with penalties for early withdrawalsForecasting techniques have been applied to demanddeposits because of their relative non-stickiness due tothe absence of contractual penalties A product withsimilar non-stickiness is credit card loans Jarrow andVan Deventerrsquos (1998) model for valuing demanddeposits and credit card loans using an arbitrage-freemethodology assumes that demand deposit balancesdepend only on the future evolution of interest rateshowever it does allow for more complexity such asmacroeconomic variables (income or unemployment)and local market or firm-specific idiosyncratic factorsJanosi et al (1999) use a commercial bankrsquos demanddeposit data and aggregate data for negotiable orderof withdrawal (NOW) accounts from the FederalReserve to empirically investigate Jarrow and Van Dev-enterrsquos model They find demand deposit balances to bestrongly autoregressive ie future balances are highlycorrelated with past balances They develop regressionmodels linear in the logarithm of balances in whichpast balances interest rates and a time trend are pre-dictive variables OrsquoBrien (2000) adds income to the setof predictive variables in the regression models Shee-han (2004) adds month-of-the-year dummy variables inthe regressions to account for calendar-specific inflows(eg bonuses or tax refunds) or outflows (eg taxpayments) He focuses on core deposits ie checkingaccounts and savings accounts distinguishes betweenthe behavior of total and retained deposits and devel-

ops models for different deposit types ie business andpersonal checking NOW savings and money marketaccount deposits

Labe and Papadakis (2010) discuss a propensityscore matching model that can be used to forecast thelikelihood of Bank of Americarsquos retail clients bringing innew funds to the firm by subscribing to promotionalofferings of CDs Such promotional CDs carry anabove-market premium rate for a limited period oftime Humphrey et al (2000) forecast the adoption ofelectronic payments in the United States they find thatone of the reasons for the slow pace of moving fromchecks to electronic payments in the United States is thecustomersrsquo perceived loss of float Many electronic pay-ment systems now address this by allowing forpayment at the due date rather than immediately

Revolving credit lines or facilities give borrowersaccess to cash on demand for short-term funding needsup to credit limits established at facility inception Bankstypically offer these facilities to corporations with in-vestment grade credit ratings which have access tocheaper sources of short-term funding for examplecommercial paper and do not draw significant amountsfrom them except

(i) for very brief periods of time under normalconditions

(ii) when severe deterioration of their financialcondition causes them to lose access to thecredit markets and

(iii) during system-wide credit market dysfunctionsuch as during the crisis of 2007ndash2009

Banks that offer these credit facilities must set asideadequate but not excessive funds to satisfy the de-mand for cash by facility borrowers Duffy et al (2005)describe a Monte Carlo simulation model that MerrillLynch Bank used to forecast these demands for cashby borrowers of their revolver portfolio The modeluses industry data for revolver usage by borrowercredit rating and assumes Markovian credit ratingmigrations correlated within and across industriesMigration probabilities were provided by a majorrating agency and correlation estimates were calcu-lated by Merrill Lynchrsquos risk group The model wasused by Merrill Lynch Bank to help manage liqui-dity risk in its multi-billion portfolio of revolvingcredit lines

Forecasting the future behavior and profitability ofretail borrowers (eg for credit card loans mortgagesand home equity lines of credit) has become a keycomponent of the credit management process Fore-casting involved in a decision to grant credit to a newborrower is known as lsquolsquocredit scoringrsquorsquo and its originsin the modern era can be found in the 1950s A dis-cussion of credit scoring models including relatedpublic policy issues is offered by Capon (1982) Fore-


casting involved in the decisions to adjust credit ac-cess and marketing effort to existing borrowers isknown as lsquolsquobehavioral scoringrsquorsquo The book by Thomaset al (2002) contains a comprehensive review of theobjectives methods and practical implementation ofcredit and behavioral scoring The formal statisticalmethods used for classifying credit applicants intolsquolsquogoodrsquorsquo and lsquolsquobadrsquorsquo risk classes is known as lsquolsquoclassifi-cation scoringrsquorsquo Hand and Henley (1997) reviewa significant part of the large body of literature inclassification scoring Baesens et al (2003) examinethe performance of standard classification algorithmsincluding logistic regression discriminant analysisk-nearest neighbor neural networks and decision treesthey also review more recently proposed ones such assupport vector machines and least-squares supportvector machines (LS-SVM) They find LS-SVM andneural network classifiers and simpler methods such aslogistic regression and linear discriminant analysis tohave good predictive power In addition to classifica-tion scoring other methods include

(i) lsquolsquoresponse scoringrsquorsquo which aims to forecast aprospectrsquos likelihood to respond to an offer forcredit and

(ii) lsquolsquobalance scoringrsquorsquo which forecasts the pros-pectrsquos likelihood of carrying a balance if theyrespond

To improve the chances of acquiring and maintainingprofitable customers offers for credit should be mailedonly to prospects with high credit response and bal-ance scores Response and balance scoring models aretypically proprietary Trench et al (2003) discuss amodel for optimally managing the size and pricing ofcard lines of credit at Bank One The model usesaccount-level historical transaction information to selectfor each cardholder through Markov decision processesannual percentage rates and credit lines that optimizethe net present value of the bankrsquos credit portfolio

42 Forecasting in Securities Brokerage Clearingand ExecutionIn the last few decades the securities brokerageindustry has seen dramatic change Traditional wire-houses charging fixed commissions evolved or werereplaced by diverse organizations offering full servicediscount and online trading channels as well as re-search and investment advisory services Thisevolution has introduced a variety of channel choicesfor retail and institutional investors Pricing servicemix and quality and human relationships are keydeterminants in the channel choice decision Firms areinterested in forecasting channel choice decisions byclients because they greatly impact capacity planningrevenue and profitability Altschuler et al (2002) dis-cuss simulation models developed for Merrill Lynchrsquos

retail brokerage to forecast client choice decisions onintroduction of lower-cost offerings to complementthe firmrsquos traditional full-service channel Clientchoice decision forecasts were used as inputs in theprocess of determining the proper pricing for thesenew offerings and for evaluating their potentialimpact on firm revenue The results of a rational eco-nomic behavior (REB) model were used as a baselineThe REB model assumes that investors optimize theirvalue received by always choosing the lowest-costoption (determined by an embedded optimizationmodel that was solved for each of millions of clientsand their actual portfolio holdings) The REB modelrsquosresults were compared with those of a Monte Carlosimulation model The Monte Carlo simulation allowsfor more realistic assumptions For example clientsrsquodecisions are impacted not only by price differentialsacross channels but also by the strength and qualityof the relationship with their financial advisor whorepresented the higher-cost options

Labe (1994) describes an application of forecastingthe likelihood of affluent prospects becoming MerrillLynchrsquos priority brokerage and investment advisoryclients (defined as clients with more than US$250000in assets) Merrill Lynch used discriminant analysis amethod akin to classification scoring to select highquality households to target in its prospecting efforts

The trading of securities in capital markets involveskey operational functions that include

(i) clearing ie establishing mutual obligations ofcounterparties in securities andor cash trades aswell as guarantees of payments and deliveriesand

(ii) settlement ie transfer of titles andor cash tothe accounts of counterparties in order to final-ize transactions

Most major markets have centralized clearingfacilities so that counterparties do not have to settlebilaterally and assume credit risk to each other Thecentral clearing organization must have robust pro-cedures to satisfy obligations to counterparties ieminimize the number of trades for which delivery ofsecurities is missed It must also hold adequate butnot excessive amounts of cash to meet paymentsForecasting the number and value of trades during aclearing and settlement cycle can help the organiza-tion meet the above objectives it can achieve this bymodeling the clearing and settlement operation usingstochastic simulation A different approach is used byde Lascurain et al (2011) they develop a linear pro-gramming method to model the clearing and settlementoperation of the Central Securities Depository ofMexico and evaluate the systemrsquos performance throughdeterministic simulation The modelrsquos formulation in deLascurain et al (2011) is a relaxation of a mixed integer


programming (MIP) formulation proposed by Guntzeret al (1998) who show that the bank clearing problemis NP-complete Eisenberg and Noe (2001) includeclearing and settlement in a systemic financial riskframework

43 Forecasting of Call Arrivals at Call CentersForecasting techniques are also used in various otherareas within the financial services sector for examplethe forecasting of arrivals in call centers which is acrucial input in the personnel scheduling processin call centers (to be discussed in a later section) Toset this in a broader context refer to the framework ofThompson (1998) for forecasting demand for servicesRecent papers that focus on forecasting call centerworkload include a tutorial by Gans et al (2003) asurvey by Aksin et al (2007) and a research paper byAldor-Noiman et al (2009)

The quality of historical data improves with thepassage of time because call centers become increas-ingly more sophisticated in capturing data with everynuance that a modeler may find useful or interestingAndrews and Cunningham (1995) describe the auto-regressive integrated moving average (ARIMA) fore-casting models used at LL Beanrsquos call centers Timeseries data used to fit the models exhibit seasonalitypatterns and are also influenced by variables such asholiday and advertising interventions Advertisingand special calendar effects are addressed by Antipovand Meade (2002) More recently Soyer andTarimcilar (2008) incorporate advertising effects bymodeling call arrivals as a modulated Poisson processwith arrival rates being driven by customer calls thatare stimulated by advertising campaigns They use aBayesian modeling framework and a data set from acall center that enables tracing calls back to specificadvertisements In a study of Fedexrsquos call centersXu (2000) presents forecasting methodologies usedat multiple levels of the business decision-makinghierarchy ie strategic business plan tactical andoperational and discusses the issues that each meth-odology addresses Methods used include exponentialsmoothing ARIMA models linear regression andtime series decomposition

At low granularity call arrival data may have toomuch noise Mandelbaum et al (2001) demonstratehow to remove relatively unpredictable short-termvariability from data and keep only predictable vari-ability They achieve this by aggregating data athigher levels of granularity ie progressively mov-ing up from minute of the hour to hour of the dayto day of the month and to month of the year Theelegant textbook assumption that call arrivals follow aPoisson process with a fixed rate that is known or canbe estimated does not hold in practice Steckley et al(2009) show that forecast errors can be large in com-

parison to the variability expected in a Poissonprocess and can have significant impact on the pre-dictions of long-run performance ignoring forecasterrors typically leads to overestimation of perfor-mance Jongbloed and Koole (2001) found that the callarrival data they had been analyzing had a variancemuch greater than the mean and therefore did notappear to be samples of Poisson distributed randomvariables They addressed this lsquolsquooverdispersionrsquorsquo byproposing a Poisson mixture model ie a Poissonmodel with an arrival rate that is not fixed but ran-dom following a certain stochastic process Brownet al (2005) found data from a different call center thatalso followed a Poisson distribution with a variablearrival rate the arrival rates were also serially corre-lated from day to day The prediction model proposedincludes the previous dayrsquos call volume as an auto-regressive term High intra-day correlations werefound by Avramidis et al (2004) who developedmodels in which the call arrival rate is a randomvariable correlated across time intervals of the sameday Steckley et al (2004) and Mehrotra et al (2010)examine the correlation of call volumes at later peri-ods of a day to call volumes experienced earlier in theday for the purpose of updating workload schedules

Methods to approximate non-homogeneous Pois-son processes often attempt to estimate the arrival rateby breaking up the data set into smaller intervalsHenderson (2003) demonstrates how a heuristic thatassumes a piecewise constant arrival rate over timeintervals with a length that shrinks as the volumeof data grows produces good arrival rate functionestimates Massey et al (1996) fit piecewise linear ratefunctions to approximate a general time-inhomoge-neous Poisson process Weinberg et al (2007) forecastan inhomogeneous Poisson process using a Bayesianframework whereby from a set of prior distributionsthey estimate the parameters of the posterior dis-tribution through a Monte Carlo Markov Chainmodel They forecast arrival rates in short intervalsof 15ndash60 minutes of a day of the week as the productof a dayrsquos forecast volume times the proportion ofcalls arriving during an interval they also allow for arandom error term

Shen and Huang (2005 2008a b) developed modelsfor inter-day forecasting and intra-day updating ofcall center arrivals using singular value decomposi-tion Their approach resulted in a significant dimen-sionality reduction In a recent empirical study Taylor(2008) compared the performance of several univari-ate time series methods for forecasting intra-day callarrivals Methods tested included seasonal autore-gressive and exponential smoothing models and thedynamic harmonic regression of Tych et al (2002)Results indicate that different methods perform bestunder different lead times and call volumes levels


Forecasting other aspects of a call center with asignificant potential for future research include forexample waiting times of calls in queues see Whitt(1999a b) and Armony and Maglaras (2004)

5 Inventory and Cash Management

51 Cash Inventory Management UnderDeterministic and Stochastic DemandOrganizations households and individuals need cashto meet their liquidity needs In the era of checks andelectronic transactions an amount of cash does nothave to be in physical currency but may correspondonly to a value in an account that has been set up forthis purpose To meet short-term liquidity needs cashmust be held in a riskless form where its value doesnot fluctuate and is available on demand but earnslittle or no interest Treasury bills and checkingaccounts are considered riskless Cash not needed tomeet short-term liquidity needs can be invested inrisky assets whereby it may earn higher returns butits value may be subject to significant fluctuations anduncertainty and could become wholly or partiallyunrecoverable Depending on the type of risky assetits value may or may not be quickly recoverable andrealizable at a modest cost (as with a public equitythat is listed in a major stock exchange) Determiningthe value of certain types of risky assets (eg privateequity real estate some hedge funds and asset-backed fixed income securities) may require special-ized valuation services which could involvesignificant time and cost Risky assets can also besubject to default in which case all or part of the valuebecomes permanently unrecoverable

Researchers have produced over the last few decadesa significant body of work by applying the principles ofinventory theory to cash management We review thecash management literature from its beginnings sowe can put later work in context and we have notidentified an earlier comprehensive review that accom-plishes this purpose Whistler (1967) discussed astochastic inventory model for rented equipment thatwas formulated as a dynamic program this workserved as a model for the cash management problemOne of the early works produced an elegant result thatbecame known as the BaumolndashTobin economic modelof the transactions demand for money independentlydeveloped by Baumol (1952) and Tobin (1956)The model assumes a deterministic constant rate ofdemand for cash It calculates the optimal lsquolsquolot sizesrsquorsquo ofthe risky asset to be converted to cash or the optimalnumbers of such conversions in the presence of trans-action and interest costs Tobinrsquos version requires aninteger number of transactions and therefore approxi-mates reality more closely than Baumolrsquos which allowsthat variable to be continuous

The concept of transactions demand for moneyaddressed by the BaumolndashTobin model is related tobut subtly different from precautionary demand forcash that applies to unforeseen expenditures oppor-tunities for advantageous purchases and uncertaintyin receipts Whalen (1966) developed a model with astructure strikingly similar to the BaumolndashTobinmodel capturing the stochastic nature of precaution-ary demand for cash Sprenkle (1969) and Akerlof andMilbourne (1978) observed that the BaumolndashTobinmodel tends to under-predict demand for moneypartly because it fails to capture the stochastic natureof precautionary demand for cash Sprenklersquos paperelicited a response by Orr (1974) which in turnprompted a counter-response by Sprenkle (1977)

Robichek et al (1965) propose a deterministic short-term financing model that incorporates a great degreeof realistic detail involved in the financial officerrsquosdecision-making process which they formulate andsolve as a linear program They include a discussionon model extensions for solving the financing prob-lem under uncertainty Sethi and Thompson (1970)proposed models based on mathematical control the-ory in which demand for cash is deterministic butdoes vary with time In an extension of the SethindashThompson model Bensoussan et al (2009) allow thedemand for cash to be satisfied by dividends anduncertain capital gains of the risky asset stock

In what became known as the MillerndashOrr modelfor cash management Miller and Orr (1966) extendedthe BaumolndashTobin model by assuming the demand forcash to be stochastic The cash balance can fluctuaterandomly between a lower and an upper boundaccording to a Bernoulli process and transactions takeplace when it starts moving out of this range units ofthe risky asset are converted into cash at the lowerbound and bought with the excess cash at the upperbound Transaction costs were assumed fixedie independent of transaction size In a critique ofthe MillerndashOrr model Weitzman (1968) finds it to belsquolsquorobustrsquorsquo ie general results do not change muchwhen the underlying assumptions are modified

Eppen and Fama (1968 1969 1971) proposed cashbalance models that are embedded in a Markovianframework In one of their papers Eppen and Fama(1969) presented a stochastic model formulated as adynamic program with transaction costs proportionalto transaction sizes Changes in the cash balance canfollow any discrete and bounded probability distribu-tion In another one of their papers Eppen and Fama(1968) developed a general stochastic model thatallowed costs to have a fixed as well as a variable com-ponent They showed how to find optimal policies forthe infinite-horizon problem using linear programmingIn their third paper Eppen and Fama (1971) proposed astochastic model with two risky assets namely lsquolsquobondsrsquorsquo


and lsquolsquostockrsquorsquo the stock is more risky but has a higherexpected return They also discussed using lsquolsquobondsrsquorsquo(the intermediate-risk asset) as a lsquolsquobufferrsquorsquo between cashand the more risky asset Taking a similar approachDaellenbach (1971) proposed a stochastic cash balancemodel using two sources of short-term funds Girgis(1968) and Neave (1970) presented models with bothfixed and proportional costs and examined conditionsfor policies to be optimal under different assumptionsHausman and Sanchez-Bell (1975) and Vickson (1985)developed models for firms facing a compensating-balance requirement specified as an average balanceover a number of days

Continuous-time formulations of the cash manage-ment problem were based on the works of Antelmanand Savage (1965) and Bather (1966) who used aWiener process to generate a stochastic demand intheir inventory problem formulations Their approachwas extended to cash management by Vial (1972)whose continuous-time formulation had fixed andproportional transaction costs and linear holding andpenalty costs and determined the form of the optimalpolicy (assuming one exists) Constantinides (1976)extended the model by allowing positive and negativecash balances determined the parameters of theoptimal policy and discussed properties of theoptimal solution Constantinides and Richard (1978)formulated a continuous-time infinite-horizondiscounted-cost cash management model with fixedand proportional transaction costs linear holding andpenalty costs and the Wiener process as thedemand-generating mechanism They proved that therealways exists an optimal policy for the cash manage-ment problem and that this policy is of a simple formSmith (1989) developed a continuous-time model witha stochastic time-varying interest rate

52 Supply Chain Management of PhysicalCurrencyPhysical cash ie paper currency and coins remainsan important component of the transactions volumeeven in economies that have experienced a significantgrowth in checks credit debit and smart cards andelectronic transactions Advantages of cash includeease of use anonymity and finality it does notrequire a bank account it protects privacy by leavingno transaction records and it eliminates the need toreceive statements and pay bills Disadvantagesof cash include ease of tax evasion support of anlsquolsquoundergroundrsquorsquo economy risk of loss through theft ordamage ability to counterfeit and unsuitability foronline transactions

Central banks provide cash to depository institu-tions which in turn circulate it in the economy Thereare studies on paper currency circulation in variouscountries For example Fase (1981) and Boeschoten

and Fase (1992) present studies by the Dutch centralbank on the demand for banknotes in the Netherlandsbefore the introduction of the Euro Ladany (1997)developed a discrete dynamic programming model todetermine optimal (minimum cost) ordering policiesfor banknotes by Israelrsquos central bank Massoud (2005)presents a dynamic cost minimizing note inventorymodel to determine optimal banknote order size andfrequency for a typical central bank

The production and distribution of banknotes andthe required infrastructure and processes have alsobeen studied Fase et al (1979) discuss a numericalplanning model for the banknotes operations at acentral bank with examples from pre-Euro Nether-lands Bauer et al (2000) develop optimization modelsfor determining the least-cost configuration of the USFederal Reserversquos currency processing sites given thetrade-off between economies of scale in processingand transportation costs In a study of costs and econ-omies of scale of the US Federal Reserversquos currencyoperations Bohn et al (2001) find that the FederalReserve is not a natural monopoly Opening currencyoperations to market competition and charging feesand penalties for some services provided for free bythe Federal Reserve at that time could lead to moreefficient allocation of resources

The movement of physical cash among centralbanks depository institutions and the public must bestudied as a closed-loop supply chain (see egDekker et al 2004) It involves the recirculation ofused notes back into the system (reverse logistics)together with a flow of new notes from the centralbank to the public through depository institutions(forward logistics) The two movements are so inter-twined that they cannot be decoupled Rajamani et al(2006) study the cash supply chain structure in theUnited States analyze it as a closed-loop supplychain and describe the cash flow management systemused by US banks They also discuss the new cashrecirculation policies adopted by the Federal Reserveto discourage banksrsquo overuse of its cash processingservices and encourage increased recirculation at thedepository institution level Among the practices to bediscouraged was lsquolsquocross shippingrsquorsquo ie shipping usedcurrency to the Federal Reserve and ordering it in thesame denominations in the same week To compareand contrast new and old Federal Reserve policies forcurrency recirculation Geismar et al (2007) introducemodels that explain the flow of currency between theFederal Reserve and banks under both sets of guide-lines They present a detailed analysis that providesoptimal policies for managing the flow of currencybetween banks and the Federal Reserve and analyzebanksrsquo responses to the new guidelines to help theFederal Reserve understand their implicationsDawande et al (2010) examine the conditions that


can induce depository institutions to respond in so-cially optimal ways according to the new FederalReserve guidelines Mehrotra et al (2010) which is apaper in this special issue of Production and OperationsManagement address the problem of obtaining effi-cient cash management operating policies fordepository institutions under the new Federal Re-serve guidelines The mixed-integer programmingmodel developed for this purpose seeks to findlsquolsquogoodrsquorsquo operating policies if such exist to quantifythe monetary impact on a depository institution op-erating according to the new guidelines Anotherobjective was to analyze to what extent the newguidelines can discourage cross shipping and stimu-late currency recirculation at the depositoryinstitution level Mehrotra et al (2010a) study pricingand logistics schemes for services such as fit-sortingand transportation that can be offered by third-partyproviders as a result of the Federal Reserversquos newpolicies

53 Other Cash Management Applications inBanking and Securities BrokerageUS banks are required to keep on reserve a minimumpercentage (currently 10) of deposits in client trans-action accounts (demand deposits and othercheckable deposits) at the Federal Reserve Until veryrecently banks had a strong incentive to keep fundson reserve at a minimum because these funds wereearning no interest Even after the 2006 Financial Ser-vices Regulatory Relief Act became law authorizingpayment of interest on reserves held at the FederalReserve banks prefer to have funds available for theirown use rather than have them locked up on reserveMoney market deposit accounts (MMDA) with check-ing allow banks to reduce the amounts on reserve atthe Federal Reserve by keeping deposits in MMDAaccounts and transferring to a companion checkingaccount only the amounts needed for transactionsOnly up to six transfers (lsquolsquosweepsrsquorsquo) per month areallowed from an MMDA to a checking account and aclientrsquos number and amount of transactions in thedays remaining in a month is unknown Therefore thesize and timing of the first five sweeps must be care-fully calculated to avoid a sixth sweep which willmove the remaining MMDA balance into the checkingaccount Banks have been using heuristic algorithmsto plan the first five sweeps This specialized inven-tory problem has been examined by Nair andAnderson (2008) who propose a stochastic dynamicprogramming model to optimize retail accountsweeps The stochastic dynamic programming modeldeveloped by Nair and Hatzakis (2010) introducescushions added to the minimum sweep amounts Itdetermines the optimal cushion sizes to ensurethat sufficient funds are available in the transaction

account in order to cover potential future transactionsand avoid the need for a sixth sweep

The impact of the sequence of transaction postingson account balances and resultant fees for insufficientfunds similar to the cost of stock-outs in inventorymanagement has been studied by Apte et al (2004)They investigate how overdraft fees and non-sufficient funds (NSF) fees interact in such situations

Brokerage houses make loans to investors who wantto use leverage ie to invest funds in excess of theirown capital in risky assets and can pledge securitiesthat they own as collateral In a simple application ofthis practice known as margin lending the brokerageextends a margin loan to a client of up to the valueof equity securities held in the clientrsquos portfolio Theclient can use the loaned funds to buy more equitysecurities Calculating the minimum value required ina clientrsquos account for a margin loan can become com-plex in accounts holding different types of securitiesincluding equities bonds and derivatives all withdifferent margin requirements The complexity in-creases even more with the presence of long and shortpositions and various derivative strategies practicedby clients Rudd and Schroeder (1982) presented asimple transportation model formulation for calculat-ing the minimum margin which represented animprovement over the heuristics used in practice Asignificant body of subsequent work has been pub-lished on this problem especially by Timkovsky andcollaborators which is more related to portfolio strat-egies and hedging We believe that the approach in thepaper by Fiterman and Timkovsky (2001) which isbased on 0ndash1 knapsack formulations is methodolog-ically the most relevant to mention in this overview

6 Waiting Line Management in RetailBanks and in Call Centers

61 Queueing Environments and ModelingAssumptionsIn financial services in particular in retail bankingretail brokerage and retail asset management (pen-sion funds etc) queueing is a common phenomenonthat has been analyzed thoroughly Queueing occursin the branches of retail banks with the tellers beingthe servers at banks of ATM machines with themachines being the servers and in call centers wherethe operators andor the automated voice responseunits are the servers These diverse queueing envi-ronments turn out to be fairly different from oneanother in particular with regard to the followingcharacteristics

(i) the information that is available to the cus-tomer and the information that is available tothe service system


(ii) the flexibility of the service system with regardto adjustments in the number of serversdependent on the demand

(iii) the order of magnitude of the number of servers

Even though in the academic literature the arrivalprocesses in queueing systems are usually assumed tobe stationary (time-homogeneous) Poisson processesarrival processes in practice are more appropriatelymodeled as non-homogeneous Poisson processes Overthe last couple of decades some research has been doneon queues that are subject to non-homogeneous Poissoninputs (see eg Massey et al 1996) The more theoret-ical research in queueing has also focused on variousaspects of customer behavior in queue in particularabandonment balking and reneging For exampleZohar et al (2002) have modeled the adaptive behav-ior of impatient customers and Whitt (2006) developedfluid models for many-server queues with abandon-ment In all three queueing environments describedabove the psychology of the customers in the queuealso plays a major role A significant amount of researchhas been done on this topic see Larson (1987) Katz et al(1991) and Bitran et al (2008) As it turns out reducingwait times may not always be the best approach in allservice encounters For example in restaurants andsalons longer service time may be perceived as betterservice In many cases customers do not like waitingbut when it comes their turn to be served would likethe service to take longer In still others for example ingrocery checkout lines customers want a businesslikepace for both waiting and service The latter categorywhich we may call dispassionate services are more com-mon in financial service situations though the formerwhich we may call hedonic services are also presentmdashforexample when a customer visits their mortgage brokeror insurance agent they would not like to be rushed Inthe following subsections we consider the variousdifferent queueing environments in more detail

62 Waiting Lines in Retail Bank Branches and atATMsThe more traditional queues in financial servicesare those in bank branches feeding the tellers Sucha queue is typically a single line with a number ofservers in parallel There are clearly no priorities insuch a queue and the discipline is just first come firstserved Such a queueing system is typically modeledas an MMs system and is discussed in many stan-dard queueing texts One important aspect of thistype of queueing in a branch is that managementusually can adjust the number of available tellersfairly easily as a function of customer demand andtime of day (This gives rise to many personnel sched-uling issues that will be discussed in the next section)

In the early 1980s retail banks began to make ex-tensive use of ATMs The ATMs at a branch of a bankbehave quite differently from the human tellers Incontrast to a teller environment the number of ATMsat a branch is fixed and cannot be adjusted as afunction of customer demand However the tellerenvironment and the ATM environment do havesome similarities In both environments a customercan observe the length of the queue and can thereforeestimate the amount of time (s)he has to wait Inneither the teller environment nor the ATM environ-ment can the bank adopt a priority system that wouldensure that more valuable customers have a shorterwait Kolesar (1984) did an early analysis of a branchwith two ATM machines and collected service timedata as well as arrival time data However it becameclear very quickly that a bank of ATMs is capable ofcollecting some very specific data automatically (egcustomer service times and machine idle times) butcannot keep track of certain other data (eg queuelengths customer waiting times) Larson (1990) there-fore developed the so-called queue inference enginewhich basically provides a procedure for estimatingthe expected waiting times of customers given theservice times recorded at the ATMs as well as themachine idle times

63 Waiting Lines in Call CentersSince the late 1980s banks have started to investheavily in call center technologies All major retailbanks now operate large call centers on a 247 basisCall centers have therefore been the subject of exten-sive research studies see the survey papers by Pinedoet al (2000) Gans et al (2003) and Aksin et al (2007)The queueing system in a call center is actually quitedifferent from the queueing systems in a teller envi-ronment or in an ATM environment there are anumber of major differences First a customer nowhas no direct information with regard to the queuelength and cannot estimate his waiting time he mustrely entirely on the information the service systemprovides him On the other hand the service organi-zation in a call center has detailed knowledge con-cerning the customers who are waiting in queue Theinstitution knows of each customer his or her identityand how valuable (s)he is to the bank The bank cannow put the customers in separate virtual queueswith different priority levels This new capability hasmade the application of priority queueing systemssuddenly very important well-known results inqueueing theory have now suddenly become moreapplicable see Kleinrock (1976) Second the callcenters are in another aspect quite different from theteller and the ATM environments The numberof servers in either a teller environment or an ATMenvironment may typically be say at most 20


whereas the number of operators in a call center maytypically be in the hundreds or even in the thousandsIn the analysis of call center queues it is now possibleto apply limit theorems with respect to the number ofoperators see Halfin and Whitt (1981) and Reed(2009) Third the banks have detailed informationwith regard to the skills of each one of its operators ina call center (language skills product knowledgeetc) This enables the bank to apply skills-based-rout-ing to the calls that are coming in see Gans and Zhou(2003) and Mehrotra et al (2009)

In call centers typically an enormous amount ofstatistical data is available that is collected automat-ically see Mandelbaum et al (2001) and Brown et al(2005) The data that are collected automatically aremuch more extensive than the data that are collectedin an ATM environment It includes the waiting timesof the customers the queue length at each point intime the proportion of customers that experience nowait at all and so on

Lately many other aspects of queueing in call cen-ters have become the subject of research This specialissue of Production and Operations Management as wellas another recent special issue contain several suchpapers For example Ormeci and Aksin (2010) focuson the effects of cross selling on the management ofthe queues in call centers They focus on the opera-tional implications of cross selling in terms of capacityusage and value generation Chevalier and Van denSchrieck (2008) and Barth et al (2010) consider hier-archical call centers that consist of multiple levels(stages) with a time-dependent overflow from onelevel to the next For example at the first stage thefront office the customers receive the basic servicesa fraction of the served customers requires more spe-cialized services that are provided by the back officeVan Dijk and Van der Sluis (2008) and Meester et al(2010) consider the service network configurationproblem Meester et al (2010) analyze networks ofgeographically dispersed call centers that vary in ser-vice and revenue-generation capabilities as well as incosts Optimally configuring a service network in thiscontext requires managers to balance the competingconsiderations of costs (including applicable dis-counts) and anticipated revenues Given the largescale of call center operations in financial servicesfirms the service network configuration problem isimportant and economically significant The approachby Meester and colleagues integrates decision prob-lems involving call distribution staffing andscheduling in a hierarchical manner (previously thesedecision problems were addressed separately)

Even though most call center research has focusedon inbound call centers a limited amount of researchhas also been done on outbound call centers Risingdelinquencies and the importance of telemarketing

has increased the need for outbound calling fromcall centers Outbound calling is quite different frominbound calling because the scheduling of calls isdone by the call center rather than the call centerbeing at the mercy of its customers This presentsunique challenges and opportunities Bollapragadaand Nair (2010) focus in this environment onimprovements in contact rates of appropriate parties

7 Personnel Scheduling in RetailBanks and in Call Centers

71 Preliminaries and General Research DirectionsAn enormous amount of work has been done onworkforce (shift) scheduling in manufacturing How-ever workforce scheduling in manufacturing is quitedifferent from workforce scheduling in servicesindustries The workforce scheduling process in man-ufacturing has to adapt itself to inventoryconsiderations and is typically a fairly regular andstable process In contrast to manufacturing indus-tries workforce scheduling in the service industrieshas to adapt itself to a fluctuating customer demandwhich in practice is often based on non-homogeneousPoisson customer arrival processes In practice adapt-ing the number of tellers or operators to the demandprocess can be done through an internal pool of flex-ible workers or through a partnership with a laborsupply agency (see Larson and Pinker 2000)

As the assignment of tellers and the hiring ofoperators depend so strongly on anticipated customerdemand a significant amount of research has focusedon probabilistic modeling of arrival processes on sta-tistical analyses of arrival processes and on customerdemand forecasting in order to accomplish a properstaffing For probabilistic modeling of customerarrival processes see Stolletz (2003) Ridley et al(2004) Avramidis et al (2004) and Jongbloed andKoole (2001) For statistical analyses of customerarrival data see Brown et al (2005) and Robbinset al (2006) For customer demand forecasting seeWeinberg et al (2007) and Shen and Huang (2008a b)

From a research perspective the personnel sched-uling problem has been tackled via a number ofdifferent approaches namely simulation stochasticmodeling optimization modeling and artificial intel-ligence The application areas considered included thescheduling of bank tellers as well as the scheduling ofthe operators in call centers Slepicka and Sporer(1981) Hammond and Mahesh (1995) and Mehrotraand Fama (2003) used simulation to schedule banktellers and call center operators Thompson (1993)studied the impact of having multiple periods withdifferent demands on determining the employee re-quirements in each segment of the schedule see alsoChen and Henderson (2001) Green et al (2007) and


Feldman et al (2008) have addressed the problem froma stochastic point of view and have developed staffingrules based on queueing theory So et al (2003) usebetter staff scheduling and team reconfiguration toimprove check processing in the Federal Reserve Bank

72 Optimization Models for Call Center StaffingMany researchers have considered this problem froman optimization point of view An optimization modelto determine the optimal staffing levels and call rout-ing can take a more aggregate form or a more detailedform Bassamboo et al (2005) consider a model with mcustomer classes and r agent pools They develop alinear program-based method to obtain optimalstaffing levels as well as call routing Not surpris-ingly a significant amount of work has also been doneon the more detailed optimization of the various pos-sible shift structures (including even lunch breaks andcoffee breaks) In large call centers there are manydifferent types of shifts that can be scheduled A shiftis characterized by the days of the week the startingtimes at the beginning of the day the ending time atthe end of the day as well as the timings of the lunchbreaks and coffee breaks Gawande (1996) (see alsoPinedo 2009) solved this staffing problem in twostages In the first stage of the approach the so-calledsolid-tour scheduling problem is solved (A solid tourrepresents a shift without any breaks a solid tour isonly characterized by the starting time at the begin-ning of the day and by the ending time at the end ofthe day) There are scores of different solid toursavailable (each one with a different starting time andending time) The demand process (the non-homoge-neous arrival process) usually can be forecast with areasonable amount of accuracy The first stage of theproblem is to find the number of personnel to hire ineach solid tour such that the total number of peopleavailable in each time interval fits the demand curveas accurately as possible This problem leads to aninteger programming formulation of a special struc-ture that actually can be solved in polynomial timeAfter it has been determined in the first stage what theactual number of people in each solid tour is theplacement of the breaks is done in the second stageTypically the break placement problem is a very hardproblem and is usually dealt with through a heuristic

Gans et al (2009) applied parametric stochasticprogramming to workforce scheduling in call centersGurvich et al (2010) have developed a chance con-strained optimization approach to deal with uncertaindemand forecasts Brazier et al (1999) applied anartificial intelligence technique namely co-operativemulti-agent scheduling on the workforce schedulingin call centers their work was done in collaborationwith the Rabobank in the Netherlands Harrison and

Zeevi (2005) developed a staffing method for large callcenters based on stochastic fluid models

Nowadays another aspect of personnel manage-ment in call centers has become important namelycall routing As the clients may have many differentdemands and the operators may have many differentskill sets call routing has gained a significant amountof interest see Mehrotra et al (2009) and Bhulai et al(2008) Optimal call routing typically has to be con-sidered in conjunction with the optimal cross trainingof the operators see Robbins et al (2007)

73 Miscellaneous Issues Concerning WorkforceScheduling in Financial ServicesVery little research has been done on workforcescheduling in other segments of the financial servicesindustry It is clear that workforce scheduling is alsoimportant in trading departments or at trading desks(equity trading foreign exchange trading or currencytrading) Such departments have to keep track of in-formation on trades such as the number of tradesdone per day for each trader amounts per trade andtotal and tradesrsquo impact with regard to risk limits ofeach trader or the entire desk Automated systemsexist for recording such information but operationalrisk mitigation practices necessitate the involvementof skilled personnel in populating and reviewing ofthe data In the case of a brokerdealer relevantpieces of information may include the commissioncharged for each trade whether the firm acted in acapacity of principal or agent and trading venue (egan exchange a dark pool or an electronic communi-cation network) Institutional and retail brokerageand asset management firms need to maintaindatabases with account-level client-level and rela-tionship-level information Typically such informationis taken from forms filled out by clients on hard copyor electronically andor from legal agreements signedby the client and the firm Skilled personnel mustperform the back-office tasks of reviewing documentsand populating the appropriate fields through userinterfaces of databases Given the regulatory implica-tions of errors in this process (eg Sarbanes-Oxley 404compliance) the personnel assigned to such tasksmust have received a rigorous specialized training Afactor that may add complexity to workforce sched-uling is the emerging trend of outsourcing suchback-office tasks especially to offshore locations inAsia or Europe In offshore outsourcing time-zonedifferences and high turnover of trained professionalscan be serious issues to contend with

As stated earlier workforce scheduling and waitingline management are strongly interconnected Alarger and better trained workforce clearly results ina better queueing performance However workforcescheduling is also strongly connected to operational


risk A larger and better trained workforce clearlyresults in a lower level of operational risk (especiallyin trading departments where human errors can havea very significant impact on the financial performanceof the institution) This topic will be discussed in moredetail in the next section on operational risk

8 Operational Risk Management

81 Types of Operational FailuresOperational risk in financial services started to receiveattention from the banking community as well as fromthe academic community in the mid-1990s Operationalrisk has since then typically been defined as the riskresulting from inadequate or failed internal processespeople and systems or from external events (BaselCommittee 2003) It covers product life cycle and exe-cution product performance information managementand data security business disruption human re-sources and reputation (see eg the General ElectricAnnual Report 2009 available at wwwgecom) Failuresconcerning internal processes can be due to transactionserrors (some due to product design) or inadequateoperational controls (lack of oversight) Information sys-tem failures can be caused by programming errors orcomputer crashes Failures concerning people may bedue to incompetence or inadequately trained employ-ees or due to fraud Actually since the mid-1990s anumber of events have taken place in financial servicesthat can be classified as rogue trading This type ofevent has turned out to be quite serious because just onesuch an event can bring down a financial services firmsee Cruz (2002) Elsinger et al (2006) Chernobai et al(2007) Cheng et al (2007) and Jarrow (2008)

82 Relationships between Operational Risk andOther Research AreasIt has become clear over the last decade that the man-agement of operational risk in finance is very closelyrelated to other research areas in operations manage-ment operations research and statistics Most of themethodological research in operational risk has fo-cused on probabilistic as well as statistical aspects ofoperational risk for example extreme value theory(EVT) (see Chernobai et al 2007) The areas of impor-tance include the following

(i) Process design and process mapping(ii) Reliability theory(iii) TQM and(iv) EVT

The area of process design and process mapping isvery important for the management of operationalrisk As discussed in earlier sections of this paperShostack (1984) focuses on process mapping in the

service industries and in particular process mappingin financial services Process mapping also includesan identification of all potential failure points Thisarea of study is closely related to the research area ofreliability theory

The area of reliability theory is very much con-cerned with system and process design issuesincluding concepts such as optimal redundanciesThis area of research originated in the aviation indus-try see the classic work by Barlow and Proschan(1975) on reliability theory One major issue in finan-cial services is the issue of determining the amount ofbackups and the amount of parallel processing andchecking that have to be designed into the processesand procedures Reliability theory has always had amajor impact on process design

Procedures that are common in TQM turn out to bevery useful for the management of operational riskfor example Six Sigma (see Cruz and Pinedo 2008)However it has become clear that there are importantsimilarities as well as differences between TQM in themanufacturing industries and operational risk man-agement in financial services One important differenceis that TQM in manufacturing (typically referred to asSix Sigma) is based on statistical properties of the Nor-mal (Gaussian distribution) because any parameterthat is being measured may have an equal probabilityof having a deviation in one direction as in the otherHowever in operational risk management the analysisof operational risk events focuses mainly on the out-liers ie the catastrophic events For that reasonthe distributions used are different from the Normalthey may be for example the Lognormal or the FatTail Lognormal

The statistical analysis often focuses on the occur-rences of rare catastrophic events because financialservices in general are quite concerned about being hitby such rare catastrophic events It is for this reasonthat EVT has become such an important research di-rection see De Haan and Ferreira (2006) EVT is basedon a limit theorem that is due to Gnedenko this limittheorem specifies the distribution of the maximum ofa series of independent and identically distributed(iid) random variables These extreme value distri-butions include the Weibull Frechet and Gumbeldistributions They typically have three parametersnamely one that specifies the mean one that specifiesthe variance and a third one that specifies the fatnessof the tail The need for being able to parameterize thefatness of the tail is based on the fact that the tail affectsthe probabilities of catastrophic events occurring

83 Operational Risk in Specific Financial SectorsThere are several sectors of the financial servicesindustry that have received a significant amount of


attention with regard to their exposure to operationalrisk namely

(i) Retail financial services (banking brokeragecredit cards)

(ii) Trading (equities bonds foreign exchange)(iii) Asset management (retail institutional)

There are several types of operational risk events thatare common in retail financial services They includesecurity breaches fraud and systems breakdownsSecurity breaches in retail financial services ofteninvolve clientsrsquo personal information such as bank ac-count numbers and social security numbers Suchevents can seriously damage an institutionrsquos reputa-tion bringing about punitive regulatory sanctions andin some well-known instances becoming the basis forclass-action lawsuits There has been an extensive bodyof research in the literature that has focused on under-standing the risk of information security (see Bagchiand Udo 2003 Dhillon 2004 Hong et al 2003 Strauband Welke 1998 Whitman 2004) Garg et al (2003) havemade an attempt to quantify the financial impact ofinformation security breaches Fraud as a cause of anoperational risk event is a major issue in the credit cardbusiness An enormous amount of work has been doneon credit card fraud detection mainly by researchers inartificial intelligence and data mining see for exampleChan et al (1999) Upgrades and consolidations of in-formation systems may be major causes of operationalrisk events associated with systems breakdowns

Operational risk events in trading may be due tohuman errors rogue trading (ie either unauthorizedtrades or illegal trades) or system breakdowns Anumber of such events have occurred in the last twodecades with catastrophic consequences for the insti-tutions involved Several rogue traders have causedtheir institutions billions of dollars in losses seeNetter and Poulsen (2003)

Asset management has also been one of the areaswithin the financial services sector that have recentlyreceived a significant amount of research attention asfar as operational risk is concerned During the finan-cial crisis of 2007ndash2009 some of the most catastrophiclosses suffered by investors resulted from failures toproperly address operational risk for example in thefraud perpetrated by Bernard Madoff (see Arvedlund2009) Operational risk can be effectively addressedby implementing robust operational infrastructuresand controls in organizations that enjoy stronggovernance as presented by Alptuna et al (2010)Operational risk is most salient in the loosely regu-lated domain of hedge funds Several studies haveshown that many of the hedge funds that have goneunder had major identifiable operational issues (egKundro and Feffer 2003 2004) Brown et al (2009a)propose a quantitative operational risk score o for

hedge funds that can be calculated from data in hedgefund databases The purpose of the o score is to iden-tify problematic funds in a manner similar to Alt-manrsquos z score which predicts corporate bankruptciesand can be used as a supplement for qualitative duediligence on hedge funds In a subsequent study thesame authors Brown et al (2009b) examine a com-prehensive sample of due diligence reports on hedgefunds and find that misrepresentation as well as notusing a major auditing firm and third-party valuationare key components of operational risk and leadingindicators of future fund failure

84 Other Research DirectionsThere are a number of other important research di-rections that already have received some researchattention and that deserve more attention in the fu-ture First how can we analyze the trade-offs betweenoperational costs (productivity) and operational riskIn the manufacturing and in the services literaturesome articles have appeared that discuss the trade-offs between costs and productivity on the one handand quality on the other hand see for example Jones(1988) and Kekre et al (2009) However this issue hasnot received much attention yet as far as the financialservices industry is concerned Second a fair amountof research has been done with regard to mitigationof operational risk The financial services industryhas thoroughly studied what the aviation industry hasbeen doing with regard to operational risk see Cruzand Pinedo (2008) In particular they have considerednear-miss management practices of operational risksee Muermann and Oktem (2002) Two more recentapproaches for mitigating operational risk include in-surance (in particular with regard to rogue traderssee Jarrow et al 2010) and securitization (eg catas-trophe bonds) see Cruz (2002) However these issuesconcerning mitigation require more study Third ithas been observed that there is a significant interplaybetween operational risk market risk and credit riskFor example when the markets are very volatile itis more likely that human errors may be made orsystems may crash These correlations have to beanalyzed in more detail in the future

The growing body of research on operational risk infinancial services presents interesting cross-fertilizationopportunities for operations management researchersgiven the increasing visibility of operational risk andthe potential losses in financial services For banks theBasel Capital Accord in a recent revision began torequire risk capital to be reserved for potentialloss resulting from operational risk Wei (2006) devel-oped models based on Bayesian credibility theoryto quantify operational risk for firm-specific capitaladequacy calculations


9 Pricing and Revenue Management

91 Pricing of Financial Services Background andAcademic ResearchFinancial services organizations expend serious effortsand resources on pricing and revenue managementApplications are diverse they include the setting of

(i) interest rates (APR) on deposits and creditproducts

(ii) trading commissions(iii) custody fees(iv) investment advisory fees(v) fund fees (which for hedge funds can be a

function of assets and performance) and(vi) insurance policy premia

Pricing and revenue management are intertwinedwith many operations management functions in largefinancial services firms because pricing stronglyaffects consumer demand for products and servicesand customer attrition Complicated pricing mecha-nisms can increase the volume of billing questions tocall centers All of these can have significant implica-tions on how these products and services are bestdelivered (eg capacity issues quality issues) as wellas on cash (inventory) management

In addition to market mechanisms pricing in finan-cial services may be driven by other factors orconstraints that may complicate or simplify it For ex-ample usury laws specify maximum interest rates to becharged for lending to consumers or businesses bybanks credit cards or pawn shops Insurance regula-tions vary by jurisdiction Fees in open-ended mutualfunds offered to US investors are governed by the In-vestment Company Act of 1940 The EmployeeRetirement Income Security Act of 1974 (ERISA) whichregulates US pension plans specifies that plan trusteesand investment advisors are considered lsquolsquofiduciariesrsquorsquowho should act in the best interests of plan participantsAmong the duties of fiduciaries is to ensure that theplan pays reasonable investment expenses includingfund advisory and custody fees and trading commis-sions Preferred pricing provisions known as MostFavored Nation (MFN) clauses are typically includedin investment management agreements of pensionplans governed by ERISA in the United States and bysimilar laws elsewhere By the fiduciary standard ofERISA transactions in accounts that hold plan assetsmust reflect the best value for the services receivedSuch services include execution of trades research in-vestment advice and anything else that may be paidthrough these transaction costs

A body of literature exists in economics and financeon some aspects of pricing in financial services andvendors offer pricing services and software but littleacademic research with an operations management

orientation has been published In the rest of this sec-tion we discuss the economic foundations of priceformation and attempt to link them to research infinance on pricing practices specific to insurancecredit and hedge funds We also review the meageroperations management literature on pricing in finan-cial services examine the challenges faced byresearchers and propose links to other research thatmight help remedy some of the issues

92 Theory of Incentives and Informational Issuesin Pricing of Financial ServicesBy the neoclassical economic assumption of rationalindividual behavior in perfect market competitionprices for financial products and services should beformed by

(i) firmsrsquo efforts to maximize profit ie revenueminus cost and

(ii) consumersrsquo desire to maximize their utilitywhen faced with exogenous prices

In this elegant model information is perfectly knownand shared by all economic agents In a setting thatmore closely approximates reality information is in-complete and asymmetrically shared making priceformation a more complex process The theory of in-centives addresses the informational issues that arise inthe principalndashagent economic relationship In such arelationship a lsquolsquoprincipalrsquorsquo delegates a set of tasks to anlsquolsquoagentrsquorsquo who possesses special competencies to performthe tasks and the two may have conflicting interestsInformational issues present in a principalndashagent rela-tionship include

(i) moral hazard whereby the agent has private in-formation that can be used to take actions toserve its interests such actions may workagainst the interests of the principal who hasto assume some of these actionsrsquo adverse con-sequences and

(ii) adverse selection where an agent uses privateinformation about its own characteristics to gainadvantage in selecting a contract offered by theprincipal

Non-verifiability is a third issue that may arise whenthe principal and the agent share information thatcannot be verified by a third party for example acourt of law The related free-rider problem refers toasymmetric sharing of benefits and costs in resourceusage among economic agents The principalndashagentmodel addresses the design of contracts with appro-priate incentives to best align the interests of principaland agent in the presence of the informational issuesof moral hazard adverse selection and non-verifiabilityLaffont and Martimort (2002) focus on the situation of


a principal dealing with a single agent to whom theprincipal offers a take-it-or-leave-it contract withoutnegotiation Their book begins with a review of theliterature on incentives in economic thought sinceSmith (1776) examined incentives in agriculture andHume (1740) explicitly defined the free-rider problemContract negotiations can be addressed by game the-ory Camerer (2003) wrote a very accessible text onbehavioral game theory in which he cites all relevantliterature The text by Tirole (1988) contains thoroughdiscussions on price formation

Moral hazard and adverse selection are responsiblefor anomalies in insurance and credit product pricingAkerlof (1970) noted that aggregate risk in segmentsof the insured population will be an increasing func-tion of insurance premium paid This is the casebecause private knowledge of individualsrsquo state ofhealth drinking or smoking habits driving behaviorstability of employment etc leads them to acceptor reject higher premia offered for health life auto-mobile or mortgage insurance The same holds truefor credit as Stiglitz and Weiss (1981) noted Higherinterest rates offered would be a lot more likely to beaccepted by borrowers whose private self-assessmentof their default probability is high These borrowerswould then become even more likely to default be-cause of the high credit cost burden Insurance andcredit are therefore rationed because there is no pricehigh enough to be profitable with certain customersThat means as Rothschild and Stiglitz (1976) foundthat equilibria in competitive insurance markets un-der imperfect and asymmetric information needed tobe specified by both price and quantity of contractsoffered Stiglitz (1977) examined differences in the roleof imperfect information in insurance pricing under amonopoly regime compared with perfect competition

93 Pricing in Asset Management SecuritiesTrading and Brokerage and Credit CardsPricing in the asset management industry consistsprimarily of fees charged on a clientrsquos assets undermanagement (AUM) in investment vehicles such asmutual funds hedge funds or separately managedaccounts (SMAs) Fees can be

(i) fixed regardless of AUM(ii) asset-based ie a percentage of AUM or

(iii) performance-based ie dependent on AUMrsquosreturn

Pricing structures reflect costs of different vehiclesare formed in the process of bringing investor de-mand into equilibrium with each vehiclersquos capacityand attempt to address the principalndashagent issues be-tween investor (principal) and investment manager(agent) Capacity of an investment vehicle refers to

(i) operational infrastructure which tends to be moresophisticated and expensive for hedge fundsthan for mutual funds and has additional com-plexities for SMAs and

(ii) implementation whereby profitable opportuni-ties become scarcer as investment vehiclesbecome larger and the effectiveness of executionof a vehiclersquos investment strategies depends to alarge degree on the market liquidity of the secu-rities traded by the investment manager in thevehiclersquos portfolio

Mutual funds and similarly managed SMAs typi-cally charge asset-based fees on a calendar basis Asassets grow due to good performance and inflows ofnew funds resulting from this good performance themanager gets rewarded for skill and effort As secu-rities traded by mutual funds are typically liquidimplementation capacity is rarely an issue In contrasthedge funds and like-managed SMAs often face capac-ity constraints that limit their size due to diminishingreturns to scale which makes the asset-based fee aninadequate incentive for hedge fund managers Hedgefund pricing therefore constitutes a more representa-tive application of the principalndashagent model because itmust use more complete incentive mechanisms to min-imize informational issues between fund manager(agent) and investor (principal) A typical pricing struc-ture for hedge funds may consist of

(i) a base (or management) fee which can be a per-centage of AUM paid on a calendar scheduleand covers operating costs of the fund and

(ii) an incentive (or performance) fee which allowsthe manager to keep a share of the value createdfor the investor during agreed-on time intervalsto ensure that the interests of the two parties arealigned

The manager earns an incentive fee if value is cre-ated for the investor according to an agreed-on metricusually based on either monetary units or rates ofreturn (see Bailey 1990) Typically incentive feearrangements have asymmetric payoffs ie theyreward gains and do not penalize losses Howeverthey often require that an investmentrsquos value be at orabove a historical maximum called a high water mark(HWM) before an incentive fee becomes payable tothe manager This implies that prior losses if anymust have been recovered Earning high incentivefees depends on fund manager skill hedge funds withhighly skilled managers have higher revenues Suchhedge funds can afford the higher expense of settingup and maintaining a robust operational infrastruc-ture and control framework as discussed by Alptunaet al (2010) Incentive fees are also common in privateequity where they are typically known as carried


interest and paid by investors on the liquidation of apartnership and distribution of proceeds

A new rule adopted by the Securities and ExchangeCommission under Section 205 of the InvestmentAdviserrsquos Act of 1940 permitted the use of perfor-mance fees by registered investment advisers Severalpublished articles then examined performance feeschemes with emphasis on inherent moral hazardand its mitigation Record and Tynan (1987) describedincentive fee arrangements and examined the basicissues involved Davanzo and Nesbitt (1987) analyzedincentive fee structures and their impact to the busi-ness of investment management Kritzman (1987)proposed ways to deal with moral hazard issuesand to reward the managerrsquos skill rather than invest-ment style or chance these issues were also addressedby Grinblatt and Titman (1987) Grinold and Rudd(1987) added another perspective by examining feestructures that would be appropriate for the investorand for the manager according to the latterrsquos invest-ment skills Bailey (1990) demonstrated that incentivefee metrics based on value added in monetary unitsserve investorsrsquo interests better than metrics based onrates of return Lynch and Musto (1997) examinedhow incentive fees compare with asset-based feesespecially in extracting value-creating effort by themanager Anson (2001) valued the call option implicitin incentive fees by BlackndashScholes analysis The non-linear optionality in incentive fees was found byLi and Tiwari (2009) to be optimal even for mutualfunds Golec and Starks (2004) examined the reduc-tion in risk levels of mutual funds after the option-likeincentive fee option was prohibited by an act of Con-gress in 1971

More recent works have studied the role of HWMsin incentive fee contracts In a seminal paper Goetz-mann et al (2003) valued incentive fee contracts withHWMs using models with closed-form solutions An-son (2001) and Lee et al (2004) examine the free-riderproblem in hedge funds that offer all investors thesame HWM late investors can avoid paying incentivefees if they enter after losses suffered by earlier in-vestors This issue was addressed in practice byoffering investors a different HWM based on theirtime of entry

Pricing for security transactions is determined in aderegulated and competitive market It has been ex-periencing a downward trend driven by the dramaticcost reductions brought by technological innovationssuch as electronic trading (see Bortoli et al 2004Levecq and Weber 2002 Stoll 2006 2008 Weber 19992006) Transaction commissions typically pay for thecosts of brokerage clearing and execution tradingresearch and investment advice before the brokeragefirm can make a profit Economies of scale are veryimportant because a brokeragersquos operational infra-

structure requires a very high capital investment andis costly to operate Generally institutions have accessto lower pricing than individual investors A pricingapplication for brokerage commissions and invest-ment advisory fees was studied by Altschuler et al(2002) They developed models to determine appro-priate commission rates for a new discount brokeragechannel and asset-based fees for advice and unlim-ited trading in full-service accounts introduced byMerrill Lynch Among the issues to contend with wereadverse selection which might prompt full-serviceclients without a strong relationship with their finan-cial advisors to select the low-cost discount channelAnother known issue is moral hazard when an ad-visor might not be committing much effort to a clientrsquosportfolio after the account was converted to the asset-based annual fee

Credit card pricing and line management werestudied together by Trench et al (2003) They built aMarkovian model to select the optimal APR and creditline for each individual cardholder based on historicalbehavior with the goal to maximize Bank Onersquosprofitability Offering an attractive APR and a largecredit line to entice a cardholder to transfer a balancepresented the moral hazard issue of the client takingadvantage of the offer and then defaulting which themodel successfully addressed In his book Phillips(2005) discusses consumer loan and insurance pricingHe expands on it in a more recent book (Phillips 2010)that also includes a chapter by Caufield (2010) whoexamines pricing for consumer credit including creditcards mortgages and auto loans In his book Thomas(2009) discusses the use of risk-based pricing in con-sumer credit Wuebker et al (2008) also wrote a bookon pricing in financial services

94 Revenue Management in Financial ServicesChallenges and OpportunitiesRevenue management principles can be applied tofinancial services pricing with some adaptation Theframework can address key considerations such asrationing in credit and insurance (see eg Akerlof1970 Phillips 2010 Stiglitz and Weiss 1981) Evidenceof rationing is reflected in protecting capacity for air-line fare classes (Talluri and Van Ryzin 2005) Anotherkey consideration is consumer behavior (Talluriand Van Ryzin 2004) Some revenue managementconcepts for example capacity can be quite differentin financial services than in the transportation andhospitality industries It can also be idiosyncratic toeach financial product for example a CD vs a mutualfund or a hedge fund Boyd (2008) discussed thechallenges faced by pricing researchers Identificationproblems exist (see Koopmans 1949 Manski 1995) thatmake it hard to build demand curves from data and


therefore figure out demand elasticities which are keyto pricing These problems are especially acute in in-dustries where sales and client relationship personshave a lot of information on customers including de-mand and price elasticities Such information remainsprivate and not centrally shared as is often the case insome areas of financial services For example firmshave databases with prices of closed sales but no in-formation on prices of refused offers the latterremains in the field Remedies have been proposedand can be considered such as Gonikrsquos (1978) whichChen (2005) analyzed and compared with a set of lin-ear contracts Tools used by Athey and Haile (2002)may also be considered

10 Concluding Remarks and FutureResearch Directions

In this paper we have attempted to present an over-view of operations management in the financialservices industry and tried to make the case that thisindustry has several unique characteristics that de-mand attention separate from research in services ingeneral We have identified a number of specific char-acteristics that make financial services unique as far asproduct design and service delivery are concernedrequiring an interdisciplinary approach In AppendixA we provide an overview table of the various op-erational processes in financial services and highlightthe ones that have attracted attention in operationsmanagement literature From the table in AppendixA it becomes immediately clear that many processesin the financial services industries have received scantresearch attention from the operational point of viewand that there are several areas that are worthy ofresearch efforts in the future These include each stepin the financial product and service life cycle as wellas in the customer relationship life cycle

Much work remains to be done on the design offinancial products so that they are

(i) easier to understand for the customer (result-ing in fewer calls to call centers)

(ii) easier to use (better online and face-to-faceinteractions with less waiting)

(iii) less prone to operational risks induced byhuman errors

(iv) easier to forecast and arrange the necessaryoperational resources for and

(v) able to take advantage of pricing and revenuemanagement opportunities

Service designs need to recognize the fact thatfinancial services are relatively sticky involvelong-term relationships with customers and are atthe same time prone to attrition due to poor perfor-mance or frustrating service encounters Anecdotal

evidence suggests that it is six times more expensiveto acquire a new client than to service an existing onemaking operations a really important factor in finan-cial services

As described in this paper there is an extensive lit-erature on traditional service operations researchtopics such as waiting lines forecasting and person-nel scheduling that are applicable to financial servicesas well Inventory models have been successfully ap-plied to cash and currency management Operationalrisk management is an emerging area that is attractingquite a lot of attention lately We expect researchers tobranch out and address other non-traditional opera-tional issues in financial services some of which wehave highlighted here Many of these are likely to becross-disciplinary interfacing information technologymarketing finance and statistics

Another area with a potentially significant payoff isthe optimization of execution costs in securities trad-ing Amihud and Mendelson (2010) and Goldsteinet al (2009) examine transaction costs in recent studiesand Bertsimas and Lo (1998) develop trading strate-gies that optimize the execution of equity transactionsA significant body of work exists in algorithmic tradingmarket microstructure and the search for liquidity insecurities markets (see eg OrsquoHara 1998) Operationsmanagement researchers can examine the problem anddevelop solutions by synthesizing elements from thisdiverse set of disciplines and points of view

Another interesting area of research could be theintegration of the various objectives for improvingoperations in financial services in which interactionsamong components can be viewed and modeledholistically For example most financial services arequite keen on improving the productivity of their pro-cesses However one has to keep in mind that there arestrong relationships between the productivity of theprocesses the operational risk encountered in theseprocesses and the quality of the services delivered tothe clients When one reduces headcount productivitymay indeed go up however the operational risk mayincrease and the quality of service may go down It is ofgreat interest to the financial services industry thatthese interdependencies are well understood

Pricing and revenue management of financialservices could be an area that is ripe for academicresearch with a potential short-term payoff that maybe large Operations management researchers couldleverage related work in economics finance and mayadapt revenue management principles to developnovel pricing methodologies for financial services

Operations management research may also be keyin enabling visionary ideas for reforming corporategovernance In the principalndashagent relationship be-tween corporate shareholders and managementproxy voting is the most important tool available to


shareholders for ensuring that management actsaccording to their interests This tool is seldom effec-tive due to shareholder apathy and obstacles to votingthrough institutional ownership of shares Holton(2006) proposed proxy aggregation mechanisms suchas a proxy exchange that could make the processmore effective in improving corporate governanceSuch mechanisms would ensure that the voting ofproxies is handled by informed entities acting in thebest interests of shareholders with thorough knowl-edge of the issues to be voted on Implementation

of proxy aggregation mechanisms is almost certainlyexpected to face complex logistical issues which op-erations management can examine and address

AcknowledgmentsThe authors thank five anonymous referees an editor andother readers for constructive comments that helped revisean earlier version of the paper The presentation contentflow and readability of the paper has benefited as a resultThe views expressed in this paper are those of the authorsand do not necessarily reflect those of Goldman Sachs

Continued

Process realm

Operational processes Strategic processes

Acquisitionorigination

Current customer portfolio

management

Delinquent customer

management Product design

Serviceprocess

design

Retail banking Campaign management Cash currency and depositmanagement

Collections Account type

offerings and design

Branch location andrationalization

Deposit product operations Teller ATM and online operations Recovery operations Outsourcing insourcing

Retail loan origination Treasury operations ATM location planning

Risk management Capacity issues

Credit liquidity operational Technology issues

Anti-money laundering surveillance

Commercial

lending

Loan syndication Credit agreement maintenance Defaulted borrower

management

Credit facility

offerings

Credit facility design

Lending operations Loan syndicate management Recovery operations Revolver term

bridge letter of

credit etc

Syndicated loan offerings

Credit risk management Risk management Outsourcing insourcing

Credit liquidityoperational

Capacity issues

Technology issues

Insurance Acquisition planning Policy maintenance Past due account

handling

Product design Product offerings

Solicitation management Claims investigation andprocessing

Recovery operations Insurance policy

types

Outsourcing insourcing

Application processing Workerrsquos comp case tracking Annuity products Capacity issues

Underwriting Property disposal

(auto auctions etc)

Technology issues

Risk management

Operational

Credit cards Campaign creation and

management

Statement operations Collections Card offerings

management

Facilities location

Credit risk management Remittance processing Recovery operations APR credit line

rewards

Processing and call

centers

Plastic printing andmailing operations

Call center management Merchant acquisition

operations

Outsourcinginsourcing


Private label cards Capacity issues

Risk management Technology issues

Credit operational

Appendix A Operations Management Processes in Financial Services


References

Akerlof G A 1970 The market for lsquolsquolemonsrsquorsquo Quality uncertaintyand the market mechanism Q J Econ 84(3) 488ndash500

Akerlof G A R D Milbourne 1978 New calculations of incomeand interest elasticities in Tobinrsquos model of the transactions de-mand for money Rev Econ Stat 60(4) 541ndash546

Aksin O Z M Armony V Mehrotra 2007 The modern call-centerA multi-disciplinary perspective on operations managementresearch Prod Oper Manag 16(6) 665ndash688

Aldor-Noiman S P D Feigin A Mandelbaum 2009 Workloadforecasting for a call center methodology and a case study AnnAppl Stat 3(4) 1403ndash1447

Alptuna U M Hatzakis R Tutuncu 2010 A best practicesframework for operational infrastructure and controls in assetmanagement Pinedo M ed Operational Control in Asset Man-

agementmdashProcesses and Costs Palgrave Macmillan New York18ndash41

Alternative Investment Management Association 2007 Guide tosound practices for European hedge fund managers Available athttpwwwaimaorgenknowledge_centresound-practicesguides-to-sound-practicescfm (accessed date August 3 2009)

Altschuler S D Batavia J Bennett R Labe B Liao R Nigam J Oh2002 Pricing analysis for Merrill Lynch Integrated Choice Inter-faces 32(1) 5ndash19

Amihud Y H Mendelson 2010 Transaction costs and assetmanagement Pinedo M ed Operational Control in AssetManagementmdashProcesses and Costs Palgrave MacmillanNew York 170ndash189

Andrews B H S M Cunningham 1995 L L Bean improves call-center forecasting Interfaces 25(6) 1ndash13

Appendix A (Continued)

Process realm




management

Delinquent customer


Serviceprocess

design

Mortgage

banking

Mortgage origination Mortgage servicing operations Collections Product offerings Facilities location

Securitization Remittance processing Foreclosure management Term APR Processing and call

centers

Secondary market

operations

Call center management Recovery operations Outsourcinginsourcing

Credit risk management Risk management Capacity issues

Credit operational Technology issues

Brokerage

investment

advisory

Client prospecting Client at risk handling Collections operations Account offeringsmanagement

Multichannel design andpricing

Cold calling referrals Client litigation Delinquent margin accounts Outsourcinginsourcing

Anti-money laundering Commission management Recovery operations Capacity issues

Margin lending Technology issues

Trust services Trading platform design

Channel choice andcoordination

Risk management

Credit operational

Anti-money laundering

Asset

management

Investment managerdue diligence

Pricing structures Collections operations New productfund

design

Organization design

Pricing structures Custodian operations Recovery operations Share classseries

management

Location of sales andclient relationshipmanagement

New account setup

processes

Fund administration Outsourcinginsourcing

Operational riskmanagement

Fund accounting Capacity issues

Anti-money laundering Valuation operations Infrastructureimplementation

Operational risk management Technology issues


Processes in bold have been addressed in operations management literature

Processes in italics have received less attention in the operations management literature


Anson M J P 2001 Hedge fund incentive fees and the lsquofree optionrsquoJ Altern Invest 4(2) 43ndash48

Antelman G R I R Savage 1965 Surveillance problems Wienerprocess Nav Res Logist Q 12(1) 35ndash55

Antipov A N Meade 2002 Forecasting call frequency at a finan-cial services call centre J Oper Res Soc 53(9) 953ndash960

Apte A U M Apte R P Beatty I C Sarkar J H Semple 2004The impact of check sequencing on NSF (not-sufficient funds)fees Interfaces 34(2) 97ndash105

Apte U R A Cavaliere S S Kulkarni 2010 Analysis and im-provement of information intensive services Evidence frominsurance claims handling Prod Oper Manag 19(6) 665ndash678

Apte U C Maglaras M Pinedo 2008 Operations in the serviceindustries Introduction to the special issue Prod Oper Manag17(3) 235ndash237

Arfelt K 2010 Lean six sigma in asset management A way to cutcosts Pinedo M ed Operational Control in Asset ManagementmdashProcesses and Costs Palgrave Macmillan New York 60ndash87

Armony M C Maglaras 2004 Contact centers with a call-back op-tion and real-time delay information Oper Res 52(4) 527ndash545

Arvedlund E 2009 Too Good to be True The Rise and Fall of BernieMadoff Portfolio Hardcover Penguin Publishing New York

Asset Managersrsquo Committee to the Presidentrsquos Working Group on Fi-nancial Markets 2009 Best practices for the hedge fund industryAvailable at httpwwwamaicmteorgPublicAMC20Report20-20Finalpdf (accessed date August 3 2009)

Athanassopoulos A D D Giokas 2000 The use of data envelopmentanalysis in banking institutions Evidence from the CommercialBank of Greece Interfaces 30(2) 81ndash95

Athey S P A Haile 2002 Identification of standard auctionmodels Econometrica 70(6) 2107ndash2140

Avramidis A N A Deslauriers P LrsquoEcuyer 2004 Modeling dailyarrivals to a telephone call center Manage Sci 50(7) 896ndash908

Bagchi K G Udo 2003 An analysis of the growth of computer andinternet security breaches Comm of AIS 12 684ndash700

Bailey J V 1990 Some thoughts on performance-based fees FinancAnal J 46(4) 31ndash40

Balasubramanian S P Konana N M Menon 2003 Customer sat-isfaction in virtual environments A study of online investingManage Sci 49(7) 871ndash889

Barlow R F Proschan 1975 Statistical Theory of Reliability and LifeTestingmdashProbability Models Holt Rinehart and Winston NewYork

Barth W M Manitz R Stolletz 2010 Analysis of two-level supportsystems with time-dependent overflowmdasha banking applicationProd Oper Manag 19(6) 757ndash768

Basel Committee 2003 Sound practices for the management andsupervision of operational risk Bank for International Settle-ments Basel Committee Publications No 96

Baesens B T Van Gestel S Viaene M Stepanova J Suykens JVanthienen 2003 Benchmarking state-of-the-art classificationalgorithms for credit scoring J Oper Res Soc 54(6) 627ndash635

Bassamboo A J M Harrison A Zeevi 2005 Dynamic routing andadmission control in high-volume service systems Asymptoticanalysis via multi-scale fluid limits J Queueing Syst 51(3ndash4)249ndash285

Bauer P W A Burnetas V CVSA G Reynolds 2000 Optimaluse of scale economies in the Federal Reserversquos currency infra-structure Econ Rev (Federal Reserve Bank of Cleveland) 36(3)13ndash27

Bather I A 1966 A continuous time inventory model J App Prob3 538ndash549

Baumol W J 1952 The transactions demand for cash An inventorytheoretic approach Q J Econ 66(4) 545ndash556

Bensoussan A A Chutani S P Sethi 2009 Optimal cash man-agement under uncertainty Oper Res Lett 37(6) 425ndash429

Berger A N D D Humphrey 1997 Efficiency of financial insti-tutions International survey and directions for future researchEur J Oper Res 98(2) 175ndash212

Bertsimas D A Lo 1998 Optimal control of execution costsJ Financ Mark 1 1ndash50

Bhulai S G Koole A Pot 2008 Simple methods for shift sched-uling in multiskill call centers Manuf Serv Oper Manage 10(3)411ndash420

Biggs J 2010 Management of risk technology and costs in a multi-line asset management business Pinedo M ed OperationalControl in Asset ManagementmdashProcesses and Costs PalgraveMacmillan New York 88ndash107

Bitran G R J-C Ferrer P Rocha e Oliviera 2008 Managing cus-tomer experiences Perspectives on the temporal aspects ofservice encounters Manuf Serv Oper Manage 10(1) 61ndash83

Black K H 2007 Preventing and detecting hedge fund failure riskthrough partial transparency Derivatives Use Trading and Reg-ulation 12(4) 330ndash341

Boeschoten W C M M G Fase 1992 The demand for large banknotes J Money Credit Bank 24(3) 319ndash337

Bohn J D Hancock P W Bauer 2001 Estimates of scale and costefficiency for Federal Reserve currency operations Econ Rev(Federal Reserve Bank of Cleveland) 37(4) 2ndash26

Bollapragada S S K Nair 2010 Improving right party contactrates at outbound call centers Prod Oper Manag 19(6) 769ndash779

Bortoli L G A Frino E Jarnecic 2004 Differences in the cost oftrade execution services on floor-based and electronic futuresmarkets J Financ Serv Res 26(1) 73ndash87

Boyd E A 2008 Challenges faced by researchers in pricing Lectureseries of the Center for Analytical Research in Technology(CART) Tepper School of Business Carnegie Mellon Univer-sity February 5 Available at httpmatteppercmuedublogp=230 (accessed date April 25 2010)

Boyer K K R Hallowell A V Roth 2002 E-services operatingstrategymdasha case study and a method for analyzing operationalbenefits J Oper Manage 20(2) 175ndash188

Brazier F M T C M Jonker F J Jungen J Treur 1999 Distributedscheduling to support a call centre A co-operative multi-agentapproach Appl Artif Intell J 13 Special Issue on Multi-AgentSystems H S Nwana and D T Ndumu (eds) 65ndash90

Brown L N Gans A Mandelbaum A Sakov H Shen S Zeltyn LZhao 2005 Statistical analysis of a telephone call centerA queueing-science perspective J Am Stat Assoc 100(469)36ndash50

Brown S J W N Goetzmann B Liang C Schwartz 2009a Es-timating operational risk for hedge funds The o score FinancAnal J 65(1) 43ndash53

Brown S J W N Goetzmann B Liang C Schwartz 2009b Trustand delegation NBER Working Paper No w15529 Available athttppapersssrncomsol3paperscfmabstract_id=1510479(accessed date September 4 2010)

Buell R D Campbell F Frei 2010 Are self-service customers sat-isfied or stuck Prod Oper Manag 19(6) 679ndash697

Camerer C F 2003 Behavioral Game Theory Experiments in StrategicInteraction Princeton University Press Princeton NJ

Campbell D F Frei 2010a Cost structure patterns in the assetmanagement industry Pinedo M ed Operational Control inAsset ManagementmdashProcesses and Costs Palgrave MacmillanNew York 154ndash168

Campbell D F Frei 2010b Cost structure customer profitabilityand retention implications of self-service distribution channelsEvidence from customer behavior in an online banking channelManage Sci 56(1) 4ndash24


Capon N 1982 Credit scoring systems A critical analysis J Mark46(2) 82ndash91

Caufield S 2010 Pricing in consumer credit Ozer O R Phillipseds Handbook of Pricing Management Oxford UniversityPress Oxford UK to appear

CFA Institute 2009 Asset Manager Code of Professional Conduct 2ndedn Available at httpwwwcfapubsorgdoipdf102469ccbv2009n81 (acceded date August 3 2009)

Chan P K W Fan A L Prodromidis S J Stolfo 1999 Distributeddata mining in credit card fraud detection IEEE Intell Syst14(6) 67ndash74

Chen B P K S G Henderson 2001 Two issues in setting callcentre staffing levels Ann Oper Res 108(1ndash4) 175ndash192

Chen F 2005 Salesforce incentives market information and pro-ductioninventory planning Manage Sci 51(1) 60ndash75

Chen P Y L M Hitt 2002 Measuring switching costs and thedeterminants of customer retention in internet enabled busi-nesses A study of the online brokerage industry Infor Syst Res13(3) 255ndash274

Cheng F N Jengte W Min B Ramachandran D Gamarnik 2007Modeling operational risk in business processes J Oper Risk2(2) 73ndash98

Chernobai A S S T Rachev F J Fabozzi 2007 OperationalRiskmdashA Guide to Basel II Capital Requirements Models andAnalysis The Frank J Fabozzi Series John Wiley and SonsNew York

Chevalier P J-C Van den Schrieck 2008 Optimizing the staffingand routing of small-size hierarchical call centers Prod OperManag 17(3) 306ndash319

Ciciretti R I Hasan C Zazzara 2009 Do internet activities addvalue Evidence from the traditional banks J Financ Serv Res35(1) 81ndash98

Clemons E K L M Hitt B Gu M E Thatcher B W Weber 2002Impacts of e-commerce and enhanced information endowmentson financial services A quantitative analysis of transparencydifferential pricing and disintermediation J Financ Serv Res22(1ndash2) 73ndash90

Constantinides G M 1976 Stochastic cash management withfixed and proportional transaction costs Manage Sci 22(12)1320ndash1331

Constantinides G M S F Richard 1978 Existence of simple pol-icies for discounted-cost inventory and cash management incontinuous time Oper Res 26(4) 620ndash636

Cook W D L M Seiford 2009 Data envelopment analysis(DEA)mdashthirty years on Eur J Oper Res 192(1) 1ndash17

Cooper W W L M Seiford K Tone 2007 Data EnvelopmentAnalysis A Comprehensive Text with Models ApplicationsReferences and DEA-Solver Software 2nd edn Springer NewYork

Cruz M 2002 Modeling Measuring and Hedging Operational RiskJohn Wiley and Sons New York

Cruz M 2010 Strategic and tactical cost management in the assetmanagement industry Pinedo M ed Operational Control inAsset ManagementmdashProcesses and Costs Palgrave MacmillanNew York 108ndash131

Cruz M M Pinedo 2008 Total quality management and opera-tional risk in the service industries Chen Z-L S Raghavaneds Chapter 7 in Tutorials in Operations Research 2008 INFORMSHanover MD 154ndash169

Cruz M M L Pinedo 2009 Global asset management Costsproductivity and operational risk J Appl IT Invest Manage1(2) 5ndash12

Cummins J D M A Weiss H Zi 1999 Organizational form andefficiency The coexistence of stock and mutual property-liabil-ity insurers Manage Sci 45(9) 1254ndash1269

Daellenbach H G 1971 A stochastic cash balance model with twosources of short-term funds Inter Econ Rev 12(2) 250ndash256

Davanzo L E S L Nesbitt 1987 Performance fees for investmentmanagement Financ Anal J 43(1) 14ndash20

Dawande M M Mehrotra V Mookerjee C Sriskandarajah 2010An analysis of coordination mechanisms for the US cash sup-ply chain Manage Sci 56(3) 553ndash570

De Almeida Filho A T C Mues L C Thomas 2010 Optimizingthe collections process in consumer credit Prod Oper Manag19(6) 698ndash708

De Haan L A Ferreira 2006 Extreme Value TheorymdashAn Introduc-tion Springer New York

de Lascurain M L de los Santos F J Herrerıa D F Munoz APalacios-Brun O Romero-Hernandez F Solıs 2011 INDEVALdevelops a new operating and settlement system using oper-ations research Working paper Instituto para el Deposito deValores Mexico City Mexico to appear in Interfaces JanuaryFebruary 2011

Dekker R M Fleischmann K Inderfurth L N Van Wassenhoveeds 2004 Reverse Logistics Quantitative Methods for Closed-LoopSupply Chains Springer-Verlag Berlin Germany

Dhillon G 2004 The challenge of managing information securityInt J Inf Manage 24(1) 3ndash4

Dijk N M van E van der Sluis 2008 To pool or not to pool in callcenters Prod Oper Manag 17(3) 296ndash305

Duffy T M Hatzakis W Hsu R Labe B Liao X Luo J Oh ASetya L Yang 2005 Merrill Lynch improves liquidity riskmanagement for revolving credit lines Interfaces 35(5) 353ndash369

Eisenberg L T H Noe 2001 Systemic risk in financial systemsManage Sci 47(2) 236ndash249

Elsinger H A Lehar M Summer 2006 Risk assessment for bank-ing systems Manage Sci 52(9) 1301ndash1314

Eppen G D E F Fama 1968 Solutions for cash-balance and sim-ple dynamic-portfolio problems J Bus 41(1) 94ndash112

Eppen G D E F Fama 1969 Cash balance and simple dynamicportfolio problems with proportional costs Int Econ Rev 10(2)119ndash133

Eppen G D E F Fama 1971 Three asset cash balance and dy-namic portfolio problems Manage Sci 17(5) 311ndash319

Fase M M G 1981 Forecasting the demand for banknotes Someempirical results for the Netherlands Eur J Oper Res 6(3) 269ndash278

Fase M M G D Van der Hoeven M Van Nieuwkerk 1979 Anumerical planning model for a central bankrsquos bank notes op-erations Stat Neerl 33(1) 7ndash25

Feldman Z A Mandelbaum W A Massey W Whitt 2008Staffing of time varying queues to achieve time-stable perfor-mance Manage Sci 54(2) 324ndash338

Fiterman A E V G Timkovsky 2001 Basket problems in margincalculation Modelling and algorithms Eur J Oper Res 129(1)209ndash223

Frei F X 2006 Breaking the trade-off between efficiency andservice Harv Bus Rev 84(11) 92ndash101

Frei F X R Kalakota A J Leone L M Marx 1999 Process vari-ation as a determinant of bank performance Evidence from theretail banking industry Manage Sci 45(9) 1210ndash1220

Fung M K 2008 To what extent are labor-saving technologies im-proving efficiency in the use of human resources Evidence fromthe banking industry Prod Oper Manag 17(1) 75ndash92

Furst K W W Lang D E Nolle 2002 Internet banking J FinancServ Res 22(1ndash2) 95ndash117

Gans N G Koole A Mandelbaum 2003 Telephone call centerstutorial Review and research prospects Manuf Serv OperManage 5(2) 79ndash141


Gans N H Shen Y-P Zhou N Korolev A McCord H Ristock2009 Parametric stochastic programming models for call-centerworkforce scheduling Available at httpfacultywashingtoneduyongpinStochastic-Workforce-Schedulingpdf (accesseddate August 22 2010)

Gans N Y-P Zhou 2003 A call-routing problem with service-levelconstraints Oper Res 51(2) 255ndash271

Garg A J Curtis H Halper 2003 Quantifying the financial impactof IT security breaches Inf Manage Comput Secur 11(2) 74ndash83

Gawande M 1996 Workforce scheduling problems with side con-straints Presentation at the semi-annual INFORMS meeting inWashington DC

Geismar N M Dawande D Rajamani C Sriskandarajah 2007Managing a bankrsquos currency inventory under new Federal Re-serve guidelines Manuf Serv Oper Manage 9(2) 147ndash167

Giloni A S Seshadri P V Kamesam 2003 Service system designfor the property and casualty insurance industry Prod OperManag 12(1) 62ndash78

Girgis N M 1968 Optimal cash balance levels Manage Sci 15(3)130ndash140

Goetzmann W N J E Jr Ingersoll S A Ross 2003 High-watermarks and hedge fund management contracts J Finance 58(4)1685ndash1718

Goldstein M A P Irvine E Kandel Z Wiener 2009 Brokeragecommissions and institutional trading patterns Rev FinancStud 22(12) 5175ndash5212

Golec J L Starks 2004 Performance fee contract change and mu-tual fund risk J Financ Econ 73(1) 93ndash118

Gonik J 1978 Tie salesmenrsquos bonuses to their forecasts Harv BusRev 56 116ndash123

Green L P Kolesar W Whitt 2007 Coping with time-varying de-mand when setting staffing requirements for a service systemProd Oper Manag 16(1) 13ndash39

Grinblatt M S Titman 1987 How clients can win the gaminggame J Portfolio Manage 13(4) 14ndash20

Grinold R A Rudd 1987 Incentive fees Who wins Who losesFinanc Anal J 43(1) 27ndash38

Guntzer M M D Jungnickel M Leclerc 1998 Efficient algorithmsfor the clearing of interbank payments Eur J Oper Res 106(1)212ndash219

Gurvich I J Luedtke T Tezcan 2010 Staffing call centers withuncertain demand forecasts A chance-constrained optimiza-tion approach Manage Sci 56(7) 1093ndash1115

Halfin S W Whitt 1981 Heavy-traffic limits for queues with manyexponential servers Oper Res 29(3) 567ndash588

Hammond D S Mahesh 1995 A simulation and analysis of bankteller manning Proceedings of the 1995 Winter Simulation Confer-ence 1077ndash1080

Hand D J W E Henley 1997 Statistical classification methods inconsumer credit scoring A review J R Stat Soc 160(3) 523ndash541

Harker P T S A Zenios 1999 Performance of financial institu-tions Manage Sci 45(9) 1175ndash1176

Harker P T S A Zenios eds 2000 Performance of Financial Institu-tionsmdashEfficiency Innovation Regulation Cambridge UniversityPress Cambridge UK

Harrison J M A Zeevi 2005 A method for staffing large callcenters based on stochastic fluid models Manuf Serv OperManage 7(1) 20ndash36

Hausman W H A Sanchez-Bell 1975 The stochastic cash balanceproblem with average compensating-balance requirementsManage Sci 21(8) 849ndash857

Henderson S G 2003 Estimation for nonhomogeneous Poissonprocesses from aggregated data Oper Res Lett 31(5) 375ndash382

Heskett J L T O Jones G W Loveman W E Sasser L A Schle-singer 1994 Putting the service-profit chain to work Harv BusRev 72(2) 164ndash174

Hitt L M F X Frei 2002 Do better customers utilize electronicdistribution channels The case of PC banking Manage Sci48(6) 732ndash748

Holton G A 2006 Investor suffrage movement Financ Anal J62(6) 15ndash20

Hong K-S Y-P Chi L R Chao J-H Tang 2003 An integratedsystem theory of information security management Inf Man-age Comput Secur 11(5) 243ndash248

Hume D A 1740 A Treatise of Human Nature Oxford UniversityPress (2000 Edition)

Humphrey D B L B Pulley J M Vesala 2000 The checkrsquos in themail Why the United States lags in the adoption of cost-savingelectronic payments J Financ Serv Res 17(1) 17ndash19

Janosi T R Jarrow F Zullo 1999 An empirical analysis of theJarrow-van Deventer model for valuing non-maturity demanddeposits J Deriv 7(1) 8ndash31

Jarrow R A 2008 Operational risk J Bank Finance 32(5) 870ndash879

Jarrow R A J Oxman Y Yildirim 2010 The cost of operationalrisk loss insurance Review of Derivatives Research 13(3) 273ndash295

Jarrow R A D R van Deventer 1998 The arbitrage-free valuationand hedging of demand deposits and credit card loans J BankFinance 22(3) 249ndash272

Jewell W S 1974 Operations research in the insurance industry 1A survey of applications Oper Res 22(5) 918ndash928

Jones P 1988 Quality capacity and productivity in service indus-tries Int J Hosp Manage 7(2) 104ndash112

Jongbloed G G M Koole 2001 Managing uncertainty in call cen-ters using Poisson mixtures Appl Stoch Models Bus Ind 17(4)307ndash318

Kantor J S Maital 1999 Measuring efficiency by product groupIntegrating DEA with activity-based accounting in a large Mid-east bank Interfaces 29(3) 27ndash36

Katz K L B M Larson R C Larson 1991 Prescription for thewaiting-in-line blues Entertain enlighten and engage SloanManage Rev 32 44ndash53

Kekre S N Secomandi E Sonmez K West 2009 Balancing riskand efficiency at a major commercial bank Manuf Serv OperManage 11(1) 160ndash173

Kleinrock L 1976 Queueing SystemsmdashVolume 2 Computer Applica-tions Wiley Interscience New York

Koetter M 2006 Measurement matters - Alternative input priceproxies for bank efficiency analyses J Financ Serv Res 30(2)199ndash227

Kolesar P 1984 Stalking the endangered CAT A Queueing analysis ofcongestion at automatic teller machines Interfaces 14(6) 16ndash26

Koopmans T C 1949 Identification problems in economic modelconstruction Econometrica 17(2) 125ndash144

Krishnan M S V Ramaswamy M C Meyer P Damien 1999Customer satisfaction for financial services The role of prod-ucts services and information technology Manage Sci 45(9)1194ndash1209

Kritzman M P 1987 Incentive fees Some problems and somesolutions Financ Anal J 43(1) 21ndash26

Kundro C S Feffer 2003 Understanding and mitigating opera-tional risk in hedge fund investments Capco Institute Whitepaper series Available at httpwwwcapcocomcontentknowledge-ideasq=contentresearch (accessed date August31 2009)

Kundro C S Feffer 2004 Valuation issues and operational risk inhedge funds Capco Institute Working paper Available at httpwwwcapcocomfilespdf7402_RISKS04_Valuation20issues


20and20operational20risk20in20hedge20fundspdf(accessed date August 31 2009)

Labe R 1994 Database marketing increases prospecting effective-ness at Merrill Lynch Interfaces 24(5) 1ndash12

Labe R I Papadakis 2010 Propensity score matching applied tofinancial service analytics Bank of America Working paper

Ladany S P 1997 Optimal banknote replenishing policy by anissuing bank Int Trans Oper Res 4(1) 1ndash12

Laffont J-J D M Martimort 2002 The Theory of Incentives ThePrincipalndashAgent Model Princeton University Press Princeton NJ

Larson R C 1987 Perspectives on queues Social justice and thepsychology of queueing Oper Res 35(6) 895ndash905

Larson R C 1990 The queue inference engine Deducing queuestatistics from transactional data Manage Sci 36(5) 586ndash601

Larson R C E J Pinker 2000 Staffing challenges in financial servicesMelnick E P Nayyar M Pinedo S Seshadri eds CreatingValue in Financial ServicesmdashStrategies Operations and Technolo-gies Kluwer Academic Publishers Norwell MA 327ndash356

Lee D K C S Lwi K F Phoon 2004 An equitable structure forhedge fund incentive fees J Investing 13(3) 31ndash43

Lee E-J D B Eastwood J Lee 2004 A sample selection model ofconsumer adoption of computer banking J Financ Serv Res26(3) 263ndash275

Levecq H B W Weber 2002 Electronic trading systems Strategicimplications of market design choices J Organ ComputElectron Commerce 12(1) 85ndash103

Li C W A Tiwari 2009 Incentive contracts in delegated portfoliomanagement Rev Financ Stud 22(11) 4681ndash4714

Lynch A W D K Musto 1997 Understanding fee structures inthe asset management business NYU Working Paper No FIN-98-050 Department of Finance Stern School of Business NewYork University

Managed Funds Association 2009 Sound practices for hedge fundmanagers Available at httpwwwmanagedfundsorgfilespdfrsquosMFA_Sound_Practices_2009pdf (accessed date August3 2009)

Mandelbaum A A Sakov S Zeltyn 2001 Empirical analysis of acall center Technical report Available at httpiew3technionacilservengReferencesccdatapdf (accessed date March 202010)

Manski C F 1995 Identification Problems in the Social Sciences Har-vard University Press Cambridge MA

Massey W A G A Parker W Whitt 1996 Estimating the param-eters of a nonhomogeneous Poisson process with linear rateTelecommun Syst 5(2) 361ndash388

Massoud N 2005 How should central banks determine and controltheir bank note inventory J Bank Finance 29(12) 3099ndash3119

Meester G A A Mehrotra H P Natarajan M J Seifert 2010Optimal configuration of a service delivery network An appli-cation to a financial services provider Prod Oper Manag 19(6)725ndash741

Mehrotra M M Dawande V Mookerjee C Sriskandarajah 2010aPricing and logistics decisions for a private-sector provider inthe cash supply chain University of Texas at Dallas Workingpaper

Mehrotra M M Dawande C Sriskandarajah 2010 A depositoryinstitutionrsquos optimal currency supply network under the Fedrsquosnew guidelines Operating policies logistics and impact ProdOper Manag 19(6) 709ndash724

Mehrotra V J Fama 2003 Call center simulation modeling Meth-ods challenges and opportunities Proceedings of the 2003 WinterSimulation Conference 1 135ndash143

Mehrotra V O Ozluk R Saltzman 2010 Intelligent procedures forintra-day updating of call center agent schedules Prod OperManag 19(3) 353ndash367

Mehrotra V K Ross G Ryder Y-P Zhou 2009 Routing to manageresolution and waiting time in call centers with heterogeneousservers Available at http facultywashingtoneduyongpinwait_and_resolutionpdf (accessed date August 22 2010)

Melnick E L P R Nayyar M L Pinedo S Seshadri 2000 CreatingValue in Financial ServicesmdashStrategies Operations and Technolo-gies Kluwer Academic Publishers Norwell MA

Menor L J A V Roth 2008 New service development competenceand performance An empirical investigation in retail bankingProd Oper Manag 17(3) 267ndash284

Menor L J A V Roth C H Mason 2001 Agility in retail bankingA numerical taxonomy of strategic service groups Manuf ServOper Manage 3(4) 273ndash292

Metters R D F X Frei V A Vargas 1999 Measurement ofmultiple sites in service firms with data envelopment analysisProd Oper Manag 8(3) 264ndash281

Meuter M L A L Ostrom R I Roundtree M J Bitner 2000 Self-service technologies Understanding customer satisfacton withtechnology-based service encounters J Mark 64(7) 50ndash64

Miller M H O Orr 1966 A model of the demand for money byfirms Q J Econ 80(3) 413ndash435

Muermann A U Oktem 2002 The near-miss management ofoperational risk J Risk Finance 4(1) 25ndash36

Nair S K R Anderson 2008 A specialized inventory problem in banksOptimizing retail sweeps Prod Oper Manag 17(3) 285ndash295

Nair S K E D Hatzakis 2010 Optimal management of sweeps toreduce sterile bank reserves University of Connecticut Workingpaper

Neave E H 1970 The stochastic cash balance problem with fixed costsfor increases and decreases Manage Sci 16(7) 472ndash490

Netter J M A B Poulsen 2003 Operational risk in financial ser-vice providers and the proposed Basel capital accord Anoverview Adv Financ Econ 8 147ndash171

Nordgard K J L Falkenberg 2010 Cost effectiveness in the assetmanagement industry An IT operations perspective PinedoM ed Operational Control in Asset ManagementmdashProcesses andCosts Palgrave Macmillan New York 132ndash150

OrsquoBrien J M 2000 Estimating the value and interest rate risk ofinterest-bearing transaction deposits FEDS Working Paper No00-53 Division of Research and Statistics Board of GovernorsFederal Reserve System

OrsquoHara M 1998 Market Microstructure Theory John Wiley and SonsMalden MA

Oral M R Yolalan 1990 An empirical study on measuring oper-ating efficiency and profitability of bank branches Eur J OperRes 46(3) 282ndash294

Ormeci E L O Z Aksin 2010 Revenue management throughdynamic cross-selling in call centers Prod Oper Manag 19(6)742ndash756

Orr D 1974 A note on the uselessness of transactions demandmodels J Finance 29(5) 1565ndash1572

Phillips R L 2005 Pricing and Revenue Optimization Stanford Uni-versity Press Stanford CA

Phillips R L 2010 Customized pricing Ozer O RL Phillips edsHandbook of Pricing Management Oxford University PressOxford UK to appear

Pinedo M L 2009 Planning and Scheduling in Manufacturing andServices 2nd edn Springer New York

Pinedo M L 2010 Global asset management An introduction to itsprocesses and costs Pinedo M L ed Operational Control inAsset ManagementmdashProcesses and Costs Palgrave MacmillanNew York 8ndash14

Pinedo M L S Seshadri G Shanthikumar 2000 Call centers infinancial services Strategies technologies and operations Mel-


nick E L P R Nayyar M L Pinedo S Seshadri eds CreatingValue in Financial ServicesmdashStrategies Operations and Technolo-gies Kluwer Academic Publishers Norwell MA 357ndash388

Rajamani D H N Geismar C Sriskandarajah 2006 A frameworkto analyze cash supply chains Prod Oper Manag 15(4) 544ndash552

Record Jr E E M A Tynan 1987 Incentive fees The basic issuesFinanc Anal J 43(1) 39ndash43

Reed J 2009 The GGIN Queue in the Halfin-Whitt Regime AnnAppl Probab 19(6) 2211ndash2269

Ridley A D W Massey M Fu 2004 Fluid approximation of apriority call center with time-varying arrivals Telecommun Rev15 69ndash77

Robbins T R D J Medeiros P Dum 2006 Evaluating arrival rateuncertainty in call centers Proceedings of the 2006 Winter Sim-ulation Conference 2180ndash2187

Robbins T R D J Medeiros T P Harrison 2007 Optimal crosstraining in call centers with uncertain arrivals and globalservice level agreements Working paper Smeal College ofBusiness Pennsylvania State University

Robichek A A D Teichroew J M Jones 1965 Optimal short termfinancing decision Manage Sci 12(1) 1ndash36

Roth A V W E Jackson III 1995 Strategic determinants of servicequality and performance Evidence from the banking industryManage Sci 41(11) 1720ndash1733

Roth A V L J Menor 2003 Insights into service operations man-agement A research agenda Prod Oper Manag 12(2) 145ndash164

Rothschild M J Stiglitz 1976 Equilibrium in competitive insur-ance markets An essay on the economics of imperfectinformation Q J Econ 90(4) 629ndash649

Rudd A M Schroeder 1982 The calculation of minimum marginManage Sci 28(12) 1368ndash1379

Sampson S E C M Froehle 2006 Foundations and implicationsof a proposed unified services theory Prod Oper Manag 15(2)329ndash343

Schneider A 2010 Managing costs at investment managementfirms Pinedo M ed Operational Control in Asset ManagementProcesses and Costs Palgrave Macmillan New York 42ndash58

Seiford L M J Zhu 1999 Profitability and marketability of the top55 US commercial banks Manage Sci 45(9) 1270ndash1288

Sethi S P G L Thompson 1970 Applications of mathe-matical control theory to finance Modeling simple dynamiccash balance problems J Financ Quant Anal 5(4ndash5) 381ndash394

Sheehan R G 2004 Valuing core deposits Working paper Depart-ment of Finance University of Notre Dame

Shen H J Z Huang 2005 Analysis of call centre arrival data usingsingular value decomposition Appl Stoch Models Bus Ind21(3) 251ndash263

Shen H J Z Huang 2008a Interday forecasting and intraday up-dating of call center arrivals Manuf Serv Oper Manage 10(3)391ndash410

Shen H J Z Huang 2008b Forecasting time series of inhomoge-neous Poisson processes with application to call centerworkforce management Ann Appl Stat 2(2) 601ndash623

Sherman H D F Gold 1985 Bank branch operating efficiencyEvaluation with data envelopment analysis J Bank Finance9(2) 297ndash315

Sherman H D G Ladino 1995 Managing bank productivity usingdata envelopment analysis (DEA) Interfaces 25(2) 60ndash73

Shostack G L 1984 Designing services that deliver Harv Bus Rev62(1) 133ndash139

Slepicka KL GA Sporer 1981 Application of simulation to thebanking industry Proceedings of the 1981 Winter Simulation Con-ference 481ndash485

Smith A 1776 An Inquiry into the Nature and Causes of the Wealth ofNations Oxford University Press Oxford UK (2008 edition)

Smith G W 1989 Transactions demand for money with a stochastictime-varying interest rate Rev Econ Stud 56(4) 623ndash633

Smith J S K R Karwan R E Markland 2007 A note on thegrowth of research in service operations management ProdOper Manag 16(6) 780ndash790

So K C C Tang R Zavala 2003 Models for improvingteam productivity at the Federal Reserve Bank Interfaces33(2) 25ndash36

Soteriou A S A Zenios 1999a Operations quality and profitabil-ity in the provision of banking services Manage Sci 45(9)1221ndash1238

Soteriou A S A Zenios 1999b Using data envelopment analysisfor costing bank products Eur J Oper Res 114(2) 234ndash248

Soyer R M M Tarimcilar 2008 Modeling and analysis of callcenter arrival data A Bayesian approach Manage Sci 54(2)266ndash278

Spohrer J P P Maglio 2008 The emergence of service scienceToward systematic service innovations to accelerate co-creationof value Prod Oper Manag 17(3) 238ndash246

Sprenkle C M 1969 The uselessness of transactions demand modelsJ Finance 24(5) 835ndash847

Sprenkle C M 1977 The uselessness of transaction demand modelsComment J Finance 32(1) 227ndash230

State Street 2009 Outsourcing investment operations Managingexpense and supporting strategic growth State Street VisionSeries May Available at httpwwwstatestreetcomknowledgevision20092009_oiopdf (accessed date August22 2009)

Steckley S G S G Henderson V Mehrotra 2004 Service systemplanning in the presence of a random arrival rate Workingpaper Department of Operations Research and IndustrialEngineering Cornell University Ithaca NY

Steckley S G S G Henderson V Mehrotra 2009 Forecast errors inservice systems Probab Eng Inf Sci 23(2) 305ndash332

Stiglitz J 1977 Monopoly non-linear pricing and imperfectinformation The insurance market Rev Econ Stud 44(3)407ndash430

Stiglitz J E A Weiss 1981 Credit rationing in markets with im-perfect information Am Econ Rev 71(3) 393ndash410

Stoll H R 2006 Electronic trading in stock markets J EconPerspect 20(1) 153ndash174

Stoll H R 2008 Future of securities markets Competition or con-solidation Financ Anal J 64(6) 15ndash26

Stolletz R 2003 Performance Analysis and Optimization of InboundCall Centers Lecture Notes in Economics and Mathematical Systems528 Springer Verlag Berlin

Straub D W R J Welke 1998 Coping with systems risk securityplanning models for management decision making MIS Q22(4) 441ndash470

Stulz R M 2007 Hedge funds Past present and future J EconPerspect 21(2) 175ndash194

Talluri K G Van Ryzin 2004 Revenue management under a gen-eral discrete choice model of consumer behavior Manage Sci50(1) 15ndash33

Talluri K T G J Van Ryzin 2005 The Theory and Practice of RevenueManagement Springer New York

Taylor J W 2008 A comparison of univariate time series methodsfor forecasting intraday arrivals at a call center Manage Sci54(2) 253ndash265

Thomas L C 2009 Consumer Credit Models Pricing Profit and Port-folios Oxford University Press Oxford UK


Thomas L C D B Edelman J N Cook 2002 Credit scoring andits applications Monographs on Mathematical Modeling ampComputation SIAM Philadelphia

Thomke S 2003 RampD comes to services Bank of Americarsquos path-breaking experiments Harv Bus Rev 81(4) 70ndash79

Thompson G 1993 Accounting for the multi-period impact ofservice when determining employee requirements for laborscheduling J Oper Manage 11(3) 269ndash287

Thompson G M 1998 Labor scheduling Part 1 Forecasting de-mand Cornell Hotel Rest Ad 39(5) 22ndash31

Tirole J 1988 The Theory of Industrial Organization The MIT PressCambridge MA

Tobin J 1956 The interest elasticity of transactions demand forcash Rev Econ Stat 38(3) 241ndash247

Trench M S S P Pederson E T Lau L Ma H Wang S K Nair2003 Managing credit lines and prices for Bank One creditcards Interfaces 33(5) 4ndash21

Tych W D J Pedregal P C Young J Davies 2002 An unobservedcomponent model for multi-rate forecasting of telephone calldemand The design of a forecasting support system Int JForecast 18(4) 673ndash695

Vassiloglou M D Giokas 1990 A study of the relative efficiency ofbank branches An application of data envelopment analysisJ Oper Res Soc 41(7) 591ndash597

Vial J-P 1972 A continuous time model for the cash balance prob-lem Szego G P K Shell eds Mathematical Models in Investmentand Finance North-Holland Amsterdam The Netherlands244ndash291

Vickson R G 1985 Simple optimal policy for cash managementThe average balance requirement case J Financ Quant Anal20(3) 353ndash369

Voss C A V Roth R B Chase 2008 Experience service opera-tions strategy and services as destinations Foundationsand exploratory investigation Prod Oper Manag 17(3)247ndash266

Weber B W 1999 Next-generation trading in futures markets Acomparison of open outcry and order matching systemsJ Manage Inf Syst 16(2) 29ndash45

Weber B W 2006 Adoption of electronic trading at the Interna-tional Securities Exchange Decis Support Syst 41(4) 728ndash746

Wei R 2006 Quantification of operational losses using firm-specificinformation and external database J Oper Risk 1(4) 1ndash33

Weinberg J L D Brown J R Stroud 2007 Bayesian forecasting ofan inhomogeneous Poisson process with applications to callcenter data J Am Stat Assoc 102(480) 1185ndash1198

Weitzman M 1968 A model of the demand for money by firmsComment Q J Econ 82(1) 161ndash164

Whalen E L 1966 A rationalization of the precautionary demandfor cash Q J Econ 80(2) 314ndash324

Whistler W D 1967 A stochastic inventory model for rentedequipment Manage Sci 13(9) 640ndash647

Whitman M E 2004 In defense of the realm Understandingthreats to information security Int J Inf Manage 24(1) 43ndash57

Whitt W 1999a Dynamic staffing in a telephone call center aimingto immediately answer all calls Oper Res Lett 24(5) 205ndash212

Whitt W 1999b Predicting queueing delays Manage Sci 45(6)870ndash888

Whitt W 2006 Fluid models for many-server queues with aban-donments Oper Res 54(1) 37ndash54

Wuebker G M Koderisch D Smith-Gallas J Baumgarten 2008Price Management in Financial Services Smart Strategies forGrowth Gower Publishing Farnham UK

Xu W 2000 Long range planning for call centers at Fedex J BusForecast Methods Syst 18(4) 7ndash11

Xue M L M Hitt P T Harker 2007 Customer efficiency channelusage and firm performance in retail banking Manuf ServOper Manage 9(4) 535ndash558

Zenios C V S A Zenios K Agathocleous A C Soteriou 1999Benchmarks of the efficiency of bank branches Interfaces 29(3)37ndash51

Zhu J 2003 Imprecise data envelopment analysis (IDEA) A reviewand improvement with an application Eur J Oper Res 144(3)513ndash529

Zohar E A Mandelbaum N Shimkin 2002 Adaptive behavior ofimpatient customers in tele-queues Theory and empirical sup-port Manage Sci 48(4) 566ndash583


Even though services account for about 84 of thetotal employment in the economy only about 4 ofthis workforce is employed in financial services Thismight come as a surprise to some because financialservices transactions in one form or another are soubiquitous in our lives Not surprisingly however thenumber of financial services firms is about 7 of thetotal non-farm firms and contributes about 13 of to-tal non-farm sales Only wholesale trade has a similaremployment and number of firms with a larger con-tribution to sales

Table 1 provides employment information for thesub-codes within financial services As can be seenretail banks insurance companies and insurance bro-kers together employ about two-thirds of the financialservices workforce A cursory look at the table gives asense of the diversity of the services sector Clearlyoperations management problems and approachesused to solve them have to be customized for partic-ular types of servicesmdashwe already know that whatworks for manufacturing may not work for servicesbut by looking at Table 1 we can also realize that whatworks for retail trade or recreational services may notwork for financial services A quick glance throughMonstercomrsquos job openings for operations managersin financial service firms shows a wide variety of ti-tles responsibilities and lsquolsquoproductsrsquorsquo related to suchjobs This is shown in Table 2 and gives a sense of thewide swath of topics that could be covered in aca-demic research on financial services operations Asfinancial services are such an important segment of

the services economy we wish to explore whetheroperations in financial services are indeed unique orshare several characteristics with services in generalThat is the motivation for this special issue

12 Distinctive Characteristics of Operations inFinancial ServicesThere are several unique operational characteristicsthat are specific to the financial services industry andthat have not been given sufficient attention in thegeneral treatment of services in the extant literatureWe list below a number of these unique operationalcharacteristics and elaborate on them in what follows

Fungible products with an extensive use oftechnology

High volumes and heterogeneity of clients Repeated service encounters Long-term contractual relationships between

customers and firms Customersrsquo sense of well-being closely inter-

twined with services Use of intermediaries Convergence of operations finance and marketing

121 Fungible Products with an Extensive Useof Technology One obvious difference betweenoperations in financial services and operations inmanufacturing and in other service industries is thatthe lsquolsquowidgetsrsquorsquo in financial services are money or relatedfinancial instruments As there is a declining use of thephysical vestiges of money such as coins currency

Table 1 Employment within Financial Services

NAICS code Title within financial services

Total employees

Number

5211 Monetary authorities-central bank

(Federal Reserve banks etc)

21510 04

5221 Depository credit intermediation

(banks credit unions etc)

1816300 307

5222 Non-depository credit intermediation

(credit cards mortgage lending etc)

659930 112

5223 Activities related to credit intermediation

(brokers for lending)

294910 50

5231 Securities and commodity contracts

intermediation and brokerage

516010 87

5232 Securities and commodity exchanges 8010 01

5239 Other financial investment activities

(mutual funds etc)

344950 58

5241 Insurance carriers 1258050 213

5242 Agencies brokerages and other

insurance-related activities

907880 153

5251 Insurance and employee benefit funds 47730 08

5259 Other investment pools and funds 41190 07

5916470 1000

Source Bureau of Labor Statistics statblsgovoeshomehtm May 2008

Table 2 A sample of Financial Service Operations Job Titles Responsi-bilities and Products

Titles Products

Vice President Operations Opera-

tions Manager Financial Operations

Supervisor Foreclosure and Bank-

ruptcy Operations Manager Risk

Operations Team Manager Team

Manager Ops Control-Fixed Income

National Director Operations Hedge

Fund Operations Specialist VP

Director Of Operations

Premiums Claims Refunds Cash

flow and treasury management

Customer statements Loan servi-

cing and support Trade

confirmations Reconciliations Tax

reporting Security settlements

Mortgages

Nature of workresponsibility

Brokerage operations Improve customer service resolves customer issues

Review security pricing Vendor support Authorize net settlement Hand-off of

data ensure data integrity Verifying transactions Tracking missing transactions

Leverage technology Maintain ops controls update policies procedures Back-

office support Understand regulations Ensure compliance Attain profit and

revenue benchmarks Reduce risk Improve quality Six sigma Operational

processing efficiency Problem solving Ensure best practices Streamline

activities Manage key expenses Work management tools Monitors work flow

Productiontesting

Source Monstercom jobs listings during the week of March 8 2009




















































and execution o

o

o


Marketing






valuation firm)









management -

-





Middleoffice




processing











servicing

Frontoffice





































































































































































Continued

Process realm




management

Delinquent customer


Serviceprocess

design










Commercial

lending


management

Credit facility

offerings



bridge letter of

credit etc




Capacity issues

Technology issues


handling




types




(auto auctions etc)

Technology issues

Risk management

Operational


management


management

Facilities location


rewards

Processing and call

centers



operations





Credit operational



References












Process realm




management

Delinquent customer


Serviceprocess

design

Mortgage

banking



centers

Secondary market

operations




Brokerage

investment

advisory








Risk management

Credit operational


Asset

management



design

Organization design


management


New account setup

processes







































































































































































































































































































































and execution o

o

o


Marketing






valuation firm)









management -

-





Middleoffice




processing











servicing

Frontoffice





































































































































































Continued

Process realm




management

Delinquent customer


Serviceprocess

design










Commercial

lending


management

Credit facility

offerings



bridge letter of

credit etc




Capacity issues

Technology issues


handling




types




(auto auctions etc)

Technology issues

Risk management

Operational


management


management

Facilities location


rewards

Processing and call

centers



operations





Credit operational



References












Process realm




management

Delinquent customer


Serviceprocess

design

Mortgage

banking



centers

Secondary market

operations




Brokerage

investment

advisory








Risk management

Credit operational


Asset

management



design

Organization design


management


New account setup

processes



























































































































































































































































































management -

-





Middleoffice




processing











servicing

Frontoffice





































































































































































Continued

Process realm




management

Delinquent customer


Serviceprocess

design










Commercial

lending


management

Credit facility

offerings



bridge letter of

credit etc




Capacity issues

Technology issues


handling




types




(auto auctions etc)

Technology issues

Risk management

Operational


management


management

Facilities location


rewards

Processing and call

centers



operations





Credit operational



References












Process realm




management

Delinquent customer


Serviceprocess

design

Mortgage

banking



centers

Secondary market

operations




Brokerage

investment

advisory








Risk management

Credit operational


Asset

management



design

Organization design


management


New account setup

processes























































































































































































































































































































































































































































Continued

Process realm




management

Delinquent customer


Serviceprocess

design










Commercial

lending


management

Credit facility

offerings



bridge letter of

credit etc




Capacity issues

Technology issues


handling




types




(auto auctions etc)

Technology issues

Risk management

Operational


management


management

Facilities location


rewards

Processing and call

centers



operations





Credit operational



References












Process realm




management

Delinquent customer


Serviceprocess

design

Mortgage

banking



centers

Secondary market

operations




Brokerage

investment

advisory








Risk management

Credit operational


Asset

management



design

Organization design


management


New account setup

processes





















































































































































































































































































References












Process realm




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Delinquent customer


Serviceprocess

design

Mortgage

banking



centers

Secondary market

operations




Brokerage

investment

advisory








Risk management

Credit operational


Asset

management



design

Organization design


management


New account setup

processes





















































































































































































































































































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