credit risk case study forrester

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Case Sd: Hp Rea EsaeEabes Cred Rsk PressasWh Bsess Resb Mke Gaer

r Appca Deepme & Prgram Maageme Pressas
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2009, Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Inormation is based on best availableresources. Opinions refect judgment at the time and are subject to change. Forrester, Technographics, Forrester Wave, RoleView, TechRadar,and Total Economic Impact are trademarks o Forrester Research, Inc. All other trademarks are the property o their respective companies. Topurchase reprints o this document, please [email protected]. For additional inormation, go to www.orrester.com.

Fr Appca Deepme & Prgram Maageme Pressas

ExECutivE SuMMARy

Uncertainty in lending regulatory requirements is putting pressure on credit risk modelers to become

more responsive. Munich-based Hypo Real Estate Group (HRE), an international banking specialist

or commercial real estate markets and the public sector, implemented a new credit risk management

approach. Te result: Application development proessionals are no longer a bottleneck, and credit

risk modelers can respond more quickly to changing regulatory requirements. HREs experiencesuggests that business rules platorms will help many nancial services rms be more responsive and

more compliant in todays uncertain regulatory environment. Application development proessionals

can learn rom HREs best practices, which are to: 1) provide risk modelers with a rules-authoring

environment and 2) create a ormal, collaborative rules lie cycle.

tABlE oF Cont EntSThe Bet And The Wort O Time For Credit

Ri Modeler

Bet Practice: HRE Proided Qant With A

Rle Athoring Enironment

Bet Practice: HRE Created A Formal,

Collaboratie Rle Lie Ccle

Bet Practice Relt: HRE Achiee Greater

Agilit In Credit Rating

WHAt it MEAnS

The Beneft O Bine Rle Are Not

Limited To Credit Ri Modeling

notES & RESouRCES

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2009, Frreser Research, ic. Reprdc PrhbedJaar 5, 2009

Rsk Pressas Wh Bsess Res


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THE BEsT AND THE WORsT OF TIMEs FOR CREDIT RIsk MODELERs

Dont blame the credit risk modelers (sometimes called quants) or the current nancial-industry

meltdown. Te credit risk models that they create are only eective i dealmakers use them, and

many did not. It is the best o times and the worst o times or quants who are responsible or

creating the methods and models or rating the risk o huge commercial real estate and public

inrastructure nancing deals:

Te good news or quants: It is the best o times or quants because having excellent credit riskmodels is more important than ever in this challenging economic environment.

Te bad news or quants: It is the worst o times or quants because the pressure is on to bemore responsive to increasing regulatory requirements, corporate risk policies, and new ways o

rating credit risk.

HRE I A Microcom O The Problem And show The Wa To A Potential soltion

One rm subject to these pressures is Hypo Real Estate (HRE) o Munich, Germany.1 HRE nances

international, commercial real estate projects and, because o its October 2007 acquisition o

Dublin-based DEPFA Bank (DEPFA), now also nances the public sector and inrastructure

projects such as roads and bridges throughout the world.2

David Kang heads HREs credit risk analytics team and has a sta o ve credit risk modelers who

are responsible or creating and maintaining about 30 models used to rate incoming and existing

deals. Tese are not the devilishly complex risk models o the investmentcredit risk modelers whom

Warren Buet described as geeks bearing ormulas.3 Rather, HRE dealmakers use Mr. Kangs

teams models to determine risk ratings or individual deals. Te more complex models can have as

many as 40 input actors, while simpler models may have only 10 input actors. Each models output

is always in the orm o a risk rating such as A+ or AA. A deals risk rating has a huge impact on

whether or not HRE closes the deal and on determining the terms o the deal.

HRE Had Two Credit Ri Approache A Third Wa Waiting In The Wing

Acquiring DEPFA gave HRE the opportunity to create a single, standard approach to credit

risk modeling or both lines o business.4 Beore proceeding, HRE was able to compare the two

companies systems:

HRE deployed models as Java Web applications. HREs previous approach to credit riskmodeling was or quants to create and test each model using Excel and then work with

application developers to build a Web-based Java application to implement the model. Tis

traditional approach is widespread at other banks. Although this approach worked, it required

quants to create methodologies and models in Excel and then communicate those requirements

to application developers who sometimes had diculty understanding models because o their


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complexity. Producing new models or making changes to existing models could take as long

as six months: Developers had to code the models in Java, and quants had to retest them once

developers had implemented them in the Java application.

DEPFA deployed models in Excel. Mr. Kangs due diligence o DEPFAs approach to credit riskmodels revealed that DEPFA used Excel spreadsheets not only to develop the models but also to

deploy them. According to Mr. Kang, it is not uncommon or quants to use Excel to create and

test models, but using Excel or deployment is unacceptable because German regulators would

not accept Excel as a proper I environment.

Te previous HRE approach lacked agility but would meet regulatory requirements which

DEPFAs more agile approach would not. What HRE needed was the best o each approach: the

exibility or credit risk modelers to create and test their own models plus the robustness o a solid

I environment (see Figure 1).

A possible solution arose in a technology DEPFA had been considering prior to the acquisition:

DEPFA had planned to replace Excel with Visual Rules, a business rules platorm oered by

Innovations Sofware echnology.5 Mr. Kangs initial examination o the capabilities o Visual Rules

led him to an unexpected revelation: I it worked, the business rules approach would not only satisy

the German regulators requirements; it would also give his group o risk modelers the ability to

create, maintain, and test the credit risk models directly using the Visual Rules authoring tool.

I was initially skeptical when Mr. Kang approached them with his ndings; I voiced concerns

about risk modelers ability to program the models in the Visual Rules environment and about

who would control deployment to production. But afer seeing a demo and working out a processwhereby I controls deployment to production, I decided to go along with the new business

rules approach because it enabled the credit risk modelers while also keeping controls in place or

deployment.

HRE Replaced Excel And Jaa With Bine Rle

In April 2008, HRE made the decision to move orward with Visual Rules and started a project to

implement the existing models o DEPFA in business rules and to run them in parallel against the

existing Excel spreadsheets. According to Mr. Kang, the new platorm works well: I think a lot

o banks dont know that something like this exists or dont trust it. In reality, using this business

rules platorm is very exible and easy. Te new business rules approach satised regulators, risk

modelers, dealmakers, and I:

Regulators are satised because the models are auditable. When HRE acquired DEPFA, it wasclear that the German regulators would not accept the use o Excel as a credit risk management

tool. Te new approach has a well-dened, auditable process or creating models and deploying

them on I-supported inrastructure. Te German regulators did not consider emailing

multiple versions o Excel spreadsheets around to be a proper I environment.


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David Kang is happy because his team can now change direction quickly when necessary.When the regulators come knocking to discuss credit risk models, Mr. Kangs group must

respond. Te business rules solution gives Mr. Kang and his team o quants the ability to

respond more quickly to changing regulatory requirements and company policies.

Figre 2 Cred Rsk Mdeers use the Ahrg t t Creae, Maa, Ad tes Mdes

Source: Forrester Research, Inc.47738


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Figre 3 Deamakers use the Cred Rsk Rag Web Appca t Ge A Rsk Rag

Source: Forrester Research, Inc.47738

Input: quantitative variables

Output: Credit rating

BEsT PRACTICE: HRE PROvIDED QuANTs WITH A RuLEs AuTHORING ENvIRONMENT

An important benet o implementing a business rules platorm is that the risk modelers can use

the business rule authoring tools to develop, change, and test the credit risk models. As Mr. Kang

explains, We are the model owners, and we know what models to implement.

Business rules platorms provide authoring exibility by oering one or more rule authoring

metaphors, which might include i-then-else statements, owcharts, decision trees, and decisiontables.6 Tese tools enable business users such as HREs quants the ability to develop the rules

themselves using Visual Rules. Visual Rules provided HRE with:

A graphical authoring tool in which creating a model is akin to designing a fowchart.Quants are extremely analytical and have a deep knowledge o statistics and nancial

mathematics. Tat kind o knowledge ofen translates well to the ability to express logic. HREs

risk modelers were able to recreate some o their models in the new tool within two weeks.


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Te ability to deploy models to HRE Web servers. Afer the models that the quants createhave been tested, I can deploy them to a production Web application. HRE I worked with

Innovations Sofware echnologies to automatically generate a Web-based user interace or the

models. I deploys nished applications on HRE servers, where credit risk analysts can access

them and then use them to determine risk ratings or potential deals.

BEsT PRACTICE: HRE CREATED A FORMAL, COLLABORATIvE RuLEs LIFE CyCLE

Using or not using the right credit risk model can make or break a deal. It is essential that credit

risk analysts use the most up-to-date credit models. Understanding this, HRE originally built its

Java Web-based application to have a central and consistent way o developing, testing, deploying,

and using the models. Business rules implementations require a lie-cycle management process that

balances the quants authoring reedom with the release management processes needed to protect

the application environment.

Deploying bug-ridden rules is more likely in scenarios involving business users who are not amiliar

with the careul processes that application developers put in place to manage changes and prevent

disastrous deployments. o prevent deploying buggy rules while maintaining modelers agility, HRE

created a rule lie-cycle process that:

Does not encumber the risk modelers. Using the Visual Rules authoring tool, risk modelershave complete independence to create new models, experiment, and test. Tey do this with

complete knowledge that when they nish their model, they will not then have to explain

the complexities o the model to a developer beore it is implemented and makes its way into

production.

Enables I to retain control o deployment. HRE developed a clearly dened yet simpleprocess in which the credit risk department hands o new models to I or deployment. I likes

that Visual Rules generates Java code and that I deploys the rules application as a JAR le just

like any other Java application.7

BEsT PRACTICE REsuLTs: HRE ACHIEvEs GREATER AGILITy IN CREDIT RATING

HREs new business rules platorm provides much greater agility by cutting out the extra

development time needed when involving I. Mr. Kang explained: With the business rulesenvironment, we dont have to involve I at all. We just do it ourselves. I I were to do it,

developers would keep coming back to us because they dont have the background in modeling.

Credit Ri Modeler Achiee Greater Agilit, Tranparenc, And Compliance

Although HRE initially set out to nd the best-o-breed credit risk application, it obtained much

more: Credit risk modelers can now respond aster to changing regulatory requirements; I is no

longer the bottleneck. HRE achieved a credit risk application that:


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Increases agility in developing, changing, testing, and deploying credit models. Enabling riskmodelers to develop and change models directly eliminates time-consuming steps such as risk

modelers translating the model to requirements or an application developer as well as the many

iterations o development and testing beore I can correctly implement the model.

Maintains consistent use o the most current models throughout the organization. Excelis a nightmare when it comes to ensuring consistent use o models.HREs Web-based Javaapplication provides a centralized way o accessing the most current models, and it was

important that HREs business rules implementation maintain that consistency. HREs

implementation o Visual Rules succeeds in this goal by using the built user-interace generator

to automatically create a Web-based Java application rom the models rules.

Improves model transparency or analysis and regulatory compliance. When risk modelers

explain new models or methods to application developers, some requirements are bound to getlost in translation. Tis method also makes it more dicult or regulators to know what is going

on once I implements the models in Java or another programming language. Because the

business rules platorm enables quants to develop rules directly, regulators can be more certain

that the quant-created models are the ones running in the business.

Application Deeloper stoc Goe up Becae The Are No Longer The Bottlenec

Te implementation o the business rules platorm and especially the tool that enables risk

modelers to program their own models took application developers out o the credit risk

modeling business. Credit risk modelers can now change their own rules, avoiding the time-

consuming step o translating the requirements to application developers who must then implement

the model in Java.

Although I no longer codes the models, it still plays a crucial role in ensuring that:

Te models are deployed on the Web inrastructure. Giving I control o the productionenvironment helps ensure that the rules lie-cycle process is ollowed which prevents

developers, whether rom the business or I, rom deploying models that have not been

properly tested.

Te quants have the support they need when using the new tool. I also has the role o

supporting the tool and the rules runtime environment. Tis can involve installing or makingavailable sofware updates, ensuring that server versions are in sync with the tools, and elding

user questions or routing them to the vendor.


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8 A recent Forrester report gives advice on how to achieve success in implementing business rules that will

empower your business users and in avoiding insidious pitalls. It recommends that organizations ollow

these our best practices: 1) assemble a team that knows business rules implementation; 2) establish a

methodology to nd your rules; 3) design with business users in mind; and 4) include business users inyour rules lie-cycle process. See the October 3, 2008, Best Practices In Implementing Business Rules

report.
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