evaluating first time defaulters from the inside out
TRANSCRIPT
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Intelligent segmentation helps lenders identiyand target new opportunities
Evaluating frst-time deaulters
From the inside out
Deloitte Center or Financial Services
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Contents1 Foreword
2 Identiying frst-time deaulters A potentially valuable segment
3 Diamonds in the rough Using analytics to tap into opportunity
6 Intelligent segmentation approach: Putting it into practice
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Deron Weston
Principal
Deloitte Consulting LLP
Nearly six months ago, we unveiled the results o a national consumer study that identifed
a growing customer segment known as frst-time deaulters. As the industry began to
look at this new customer, banks began asking how and what could be done to address
these particular customers needs while making them a proftable contributor to the
organizations revenues.
This paper aims to shed light on ways bank can interact with frst-time deaulters. In
particular, it ocuses on how banks and lenders can use data analytics to identiy and
retain these nuanced customers to build proftable, long-term relationships.
Applying a predictive modeling approach to current and prospective customers can give
fnancial institutions tools to defne customer needs and risk. Like the general population
o banking customers, frst-time deaulters can be evaluated across the customer
development liecycle but with implied dierences involving customer acquisition,
customer servicing, cross- and up-selling, and custome retention.
Once frst-time deaulters have been identifed, banks may create oers that improve
short- and long-term proftability by using an approach based on collecting, ormatting
and manipulating data, identiying customer segments, and defning value propositions
or each identifed segment.
Using these enhanced capabilities may allow banks and lenders to eectively target,acquire and retain liquidity-seeking frst-time deaulters in a challenging market.
Regards,
Andrew Freeman
Executive Director
Deloitte Center or Financial Services
Foreword
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Since the most recent economic crisis, many US consumers
have experienced signifcant fnancial hardship. Many
Americans have ound themselves without a job, behind
on their mortgage or unable to keep up with credit card
payments. Some o these individuals, previously with a
good credit standing, became delinquent or deaulted on
their debt obligations or the frst time. According to a
survey conducted by the Deloitte Center or Financial
Services, ully 22% o Americans with bank accountsexperienced a serious negative credit situation during the
last two years, hal o them or the frst time in their credit
histories.1
Financial institutions and their customers appear to be
gradually recovering rom the recession, and the
contraction in the retail credit markets appears to be
easing. Lenders have begun to look or new ways to
revitalize their lending businesses. Oers to riskier
borrowers have been increasing,2 as fnancial institutions
may have realized that a larger-than-normal portion o the
credit-challenged population may not have been reckless
borrowers, even i they did experience a negative creditsituation. Over time, these indiv iduals may continue to
improve their fnancial standing and seek to avoid uture
credit problems by deleveraging, limiting excess
consumption, and increasing their savings. This is the
segment we reer to as frst-time deaulters.
Who are the frst-time deaulters?
Those who had a negative credit experience, such as a
delinquency, oreclosure, bankruptcy, and/or charge-o,
or the frst time since September 2008.3
Those who were more likely to miss their credit
obligations as a result o macroeconomic conditions
(such as unemployment and reduced income) than poor
decision-making or a lack o fnancial discipline.
Those with a greater propensity to seek out loans in the
uture. In need o credit, frst-time deaulters were more
likely to obtain loan products than their prime
counterparts, possibly making them a source o
much-needed revenue or lenders in the uture.
Some leading-practice banks employ various degrees o
sophistication related to data analytics, but in general
there are many opportunities or the industry to adopt
these practices. Specifcally, i banks can identiy frst-time
deaulters in their customer base, a particular opportunity
exists to acquire long-term customers with avorable risk/return characteristics. For example, one large fnancial
institution is testing a targeted credit card oering,
designed or customers whose credit was damaged during
the recession. Borrowers are required to link their credit
card account to a checking, savings, or brokerage account
so that the fnancial institution can withdraw money rom
that source i a payment is missed. Meanwhile, use o the
card helps the customer to rebuild his or her credit score.
Also, in the third quarter o 2010, there was a signifcant
increase rom 7% o total oers in 2009 to 17% in 2010 in the
number o credit card oers to previously prime customers
with blemished credit.4 This share is expected to increase
urther during 2011. Additionally, banks reported an
increased willingness to make consumer installment loans.5
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Please see www.deloitte.com/us/about or a detailed description o the legal structure o Deloitte LLP and its subsidiaries. Certain services may
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2
Identiying frst-time deaulters A potentially valuable segment
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How can fnancial institutions take advantage o this
market opportunity? Advanced data analytics can be used
to identiy, acquire, solicit, and retain frst-time
deaulters who have the potential to become valuable
long-term banking customers. Advanced analytics is the
process o converting a wealth o data into actionable
insights through statistical and mathematical models.
Using a predictive modeling approach that ocuses oncurrent and prospective customers, internal data can be
supplemented with a variety o external data sets, giving
organizations the tools to defne customer needs and risk.
This can be particularly eective in segmenting potential
customers who may appear to have similar characteristics.
Some members o this population may in act have specifc
characteristics that help identiy them as candidates to
become good long-term customers with high value to the
organization. For example, among a group o apparently
similar 22 to 32 year olds who are in deault, an individual
whose characteristics include a certain career feld,
education level, or geographic location might have the
potential to become a valuable customer.
How can these diamonds in the rough be uncovered?
For existing customers, data rom traditional internal
sources, such as historical account activity and payment
perormance, can be combined with nontraditional
external individual or household-level data sources, such
as liestyle data (e.g., interest in health, sports preerences,
magazine or newspaper subscriptions, type o work, etc.),
retail purchase patterns (e.g., average likely market basket,
eating-out spend, etc.), social media (i.e., personal datagenerated rom social media/networks used to create
more personalized products), U.S. Census data, etc.
For potential new customers, banks can also make use o
credit bureau data, looking at individual borrowing
records, the trajectory o their credit score, and the
number o bureau inquiries among other metrics. Armed
with this inormation, organizations can unlock new
insights into customer populations by using analytics to
apply a customer lietime value model to create and
evaluate variables, develop predictive models, and score
individual profles (Exhibit 1).
Exhibit 1
Using analytics to unlock insights into customer populations
Innovativedata sources
Businessvalue
Modeling
Customer acquisition
Customer retention
Cross- and up-selling
Customer servicing
Data aggregation
and data cleansing
Predictive analytics
Evaluate and
create variables
Develop
predictive
models
Score individual
profiles
Nontraditional data unlock new insights into
customer populations
Traditionalinternal data
sources
Nontraditionalexternal individual orhousehold-level data
sources
Lifestyle
FinancialBehavioral
Household
Consumer
Acquisitioncost/retention
Customertransactions
Productmix/margin
Source: Deloitte Consulting LLP
Evaluating frst-time deaulters From the inside out 3
Diamonds in the rough Using analytics to tap into opportunity
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4
Customer acquisition
Data analytics can help fnancial organizations to move
beyond traditional likely to buy marketing models to
identiy customers who have a specifc need. Improved
customer segmentation acilitates more eective targeting
and acquisition eorts. This insight can allow banks to
ocus their resources on customers who oer the most
signifcant long-term potential to the organization.
When evaluating frst-time deaulters or potential
acquisition, fnancial institutions can use analytics to
identiy those with solid potential by leveraging data in new
ways. For example, fnancial institutions can consider
credit-score change and degradation in conjunction with
worsening employment indicators or a certain proession
in a certain geography and changes in purchase patterns
(e.g., journal or magazine subscription cancellations), as
well as the specifc products that experience delinquency,
such as a credit card or an adjustable rate mortgage.
Ater identiying those customers who present the most
avorable profles, banks can use traditionalcommunications and direct marketing activities such as
mail, direct mail, or online promotions to attract these
potential customers more eectively. As an additional
beneft, data analytics may be used to determine a measure
o return on marketing investment and help banks most
eectively allocate their marketing budget spend.
Early adopters can capitalize on the demand rom frst-time
deaulters who are looking or fnancial products, whether
credit cards, savings and checking accounts, or home or car
loans, at well above average pricing (within regulatory
limits6). There is little evidence in the market that lenders
are currently targeting frst-time deaulters.
Customer servicing
Data analytics may provide a deeper understanding o the
behavioral and fnancial characteristics o current and
uture customers. Financial institutions can now improve
day-to-day management o existing accounts and address
needs that are particular to frst-time deaulters. Through
predictive statistical models, fnancial institutions could
potentially anticipate specifc needs, proactively meet those
needs, and potentially improve customer retention.
Delivering customer service eectively improves the lietime
value o the customer, whether this service includes
providing a single point o contact or waiving account ees.
As expected, customer satisaction among individuals with
recent credit problems is very low,7 as many banks are
trying to end or have ended their relationships with
customers in this segment. However, as the economy
recovers and jobs rebound, the fnancial situation o theseindividuals may also begin to improve, and with it their
need to have access to credit cards, home loans, mutual
unds, certifcates o deposit, and more.
For example, a frst-time deaulter with a low credit score
may have a desire to rebuild a positive credit history. He or
she may value the opportunity to learn more about saving
and budgeting, setting up automatic debit or recurring
expenses, or signing up or electronic spending alerts. Over
time, as creditworthiness improves, card limits may be
increased, rates lowered, and additional opportunities may
be presented.
Cross- and up-selling
Once a fnancial institution has identifed frst-time
deaulters with the potential to become high-value,
long-term customers, analytics could then be used to
determine eective and proftable ways o expanding high
potential relationships through models that predict lietime
customer value and likelihood o attrition or potential and
intent to buy additional products. Integrated customer
behavior, demographic, and attitudinal data can help banks
to understand customer needs and make the right oers.
The recovering frst-time deaulters specifc needs may
drive fnancial institutions account targeting and new
design oerings. Once a positive credit history has been
reestablished and the customer is on a more solid fnancial
ooting, he or she may be looking or new car or home
loans, IRAs, or fnancial instruments with higher yields.
By disseminating predictive analytics results throughout the
enterprise, lenders can provide a more consistent customer
experience across various channels and can seek to improve
customer value.
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Evaluating frst-time deaulters From the inside out 5
Customer retention
Sophisticated data analytics such as evolutionary
segmentation solutions that account or customer
demographics, attitudes, buying patterns, etc. can help
fnancial institutions to identiy customers who are most
likely to move their accounts to other institutions. Armed
with this inormation, fnancial organizations can develop
customer-specifc retention tactics that are consistent
with current and expected lietime value. For example,lenders might oer lower rates or higher credit limits to
those customers who have improved their fnancial
standing, or communicate additional product and service
oerings that address the individuals needs as his
fnancial situation improves.
Although many frst-time deaulters may recover and
resolve the personal situations that resulted in credit issues,
a subset may become repeat deaulters, making them
unproftable customers. Predictive analytics can help
enable banks to identiy those customers who remain at
risk and take necessary corrective actions to help prevent
charge-os. Credit policies and models may need to beupdated with application data variables that isolate the
one-time deaulter rom the ongoing bad credit risk, such
as job history, employment industry, personal liquidity, and
product types that may have caused problems (such as
adjustable-rate mortgages). This may result in a shit
towards more undamental underwriting that considers a
number o actors in addition to a credit bureau score.
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Exhibit 2
Customer segmentation approach
Source: Deloitte Consulting LLP
Propensity to buy,retention/churn
models
Profitability/valuemodel
Development of
clusters/segments
based on the
aggregation of data
provided and
models developed.
Internal data may be
enhanced withexternal information,
which lets the
expanded dataset
speak and helps
create segments
based on value,
propensity to buy, and
other factors.
3. Define value propositions
for each identified segment
ProfitabilityPropensity to buy Segment profile 1
Business model description A
Value proposition ASegment 1
2. Identify customer segments1. Collect, format, and manipulate data
ProfitabilityPropensity to buy
Segment profile 1Business model description A
Value proposition BSegment 2
ProfitabilityPropensity to buy
Segment profile 1Business model description A
Value proposition XSegment N
External
demographics andpsychographicsdata
Historical account,product, and
customerdata
Test /control pilots to refine models and maximize
predictive performance
6
Intelligent segmentation approach Putting it into practice
One approach to acquiring, cross-selling, and up-selling
frst-time deaulters uses intelligent segmentation methods
to identiy and evaluate frst-time deaulter prospects. The
more eective the segmentation, the more eective the
analytics may be at targeting a quality customer. Many
variables can be considered or segmentation, including
home-loan balance, income, and situation that caused the
deault. Ater the frst-time deaulters have been identifed,
the next step is to create oers or these prospects that canimprove the fnancial institutions short- and long-term
proftability and market share.
This approach is based on three steps (Exhibit 2):
1. Collect, ormat, and manipulate data. Gather historical
account, product and customer data, external
demographics, and psychographics data and evaluate as
it relates to pre-underwriting/proftability model and
propensity to buy models. This segmentation helps to
confrm that prospective customers have a high likelihood
o wanting to buy products or open an account and are
within the fnancial institutions risk tolerances.
2. Identiy customer segments. Develop customer
clusters based on the preliminary risk profle along with
potential proftability and propensity to buy using
unbiased, assumption-ree analytical methods.
3. Defne value propositions or each identifedsegment. Target the customer segments identifed as
potentially proftable with customized oerings that they
are likely to buy and may become proftable to the bank.
Several well-known banks and other fnancial institutions
have leveraged the benefts o analytics in identiying likely
prospects or credit cards or other fnancial products and
oering an opportunity to add proftable long-term
customers.
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Evaluating frst-time deaulters From the inside out 7
Example: Growing a proftable credit card market
A credit card issuer was trying to grow proftability in the
low-income segment in Latin America, but risk
management challenges, such as poor collection
perormance and high credit losses, had inhibited results.
The card issuer wanted to provide tools to its card-issuing
banks to help them identiy the most avorable customers.
The card issuer developed predictive models to help creditcard issuers and processors improve their collection
perormance. The card bank wanted to be able to identiy
and classiy frst-time deaulters based on their probability
o reestablishing a sound fnancial ooting or accepting
repayment agreements, as well as to improve its collections
strategy.
Predictive classifcation models helped the issuer to
separate frst-time deaulters rom chronic deaulters.
Several scoring models were created to predict the
probability o a given customer moving rom delinquency
to a positive credit standing. A predictive model was
created that orecast the likelihood o a delinquentcustomer to accept a repayment agreement and delivered a
decision-tree optimization tool that helps increase the
eectiveness o a collection strategy. The eectiveness o
the issuers collections process rose signifcantly ater the
application o these models.
Example: International bank improves value o
customer contacts
The marketing policy o a large international bank limited
the number o customer contacts that could be made
each year. As a result, prime customers oten received
marketing communications rom the most timely product
group, but not or products that were the most relevant
and proftable. For example, they might receive a series o
oers during the frst part o the year, instead opromotions targeted to the specifc interests o a prime
customer, such as special rates on second homes, premium
credit card oerings, or mutual unds.
The bank needed a way to analyze customer behavior to
determine the next desirable product oer or a specifc
segment based on their current situation. The bank had
very large amounts o data to be mined, including more
than 1,000 attributes and variables or more than 9 million
customers.
By using data analytics, the banks customers were scored
and assigned to oer clusters. More than 3 millionprioritized-oer candidates were identifed and submitted,
and 40 cluster segments were developed.
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8
Conclusion
As the economic climate evolves, banks and other fnancial institutions have an
opportunity to identiy customers among a unique segment o the market known as
frst-time deaulters. Data analytics provide a valuable tool to help identiy and target
the individuals who oer the most likelihood o long-term potential as proftable
customers, in addition to providing insight regarding the most eective products and
services to oer them. By applying data analysis to existing fnancial and third-party data,
fnancial institut ions may be able to maximize potential and minimize risk in approaching
this market segment.
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Evaluating frst-time deaulters From the inside out 9
Contacts
Andrew Freeman
Executive Director
Deloitte Center or Financial Services
+1 212 436 4676
Deron Weston
Principal
Deloitte Consulting LLP
+1 404 631 3519
Omer Sohail
Principal
Deloitte Consulting LLP
+1 214 840 7220
Leandro Dalle Mule
Senior Manager
Deloitte Consulting LLP
+1 617 437 3449
End notes1 Deloitte Center or Financial Services, First-Time Deaulters: An underappreciated customer segment or
lenders? February 2011.
2
More Card O ers or Consumers w ith Lower Credit Scores, credit.com, Dec. 16, 2010.
3 For the purpose o this discussion, a deault reers to one or more o the ollowing events: three or more
times late on a mortgage, three or more times late on a loan other than a mortgage, three or more times
late on a credit card bill, bankruptcy, oreclosure, being contacted by a collections agency, been delinquent
on child support, delinquent on taxes, delinquent on medical bills, legal judgments, or charge-os.
4 More Card O ers or Consumers w ith Lower Credit Scores, credit.com, Dec. 16, 2010.
5Senior Loan Ofcer Opinion Survey on Bank Lending Practices, Federal Reserve, January 2011.
6 Subject to the regulations defned by the CARD Act o 2009 and the Dodd-Frank Act.
7 First-time deaulter s: Changes on the hor izon, Deloitte Center or Financial Ser vices, July 2011.
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