Evaluating First Time Defaulters From the Inside Out

8/3/2019 Evaluating First Time Defaulters From the Inside Out

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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


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

As used in this document, Deloitte means Deloitte Services LP and Deloitte Consulting LLP, which are separate subsidiaries o Deloitte LLP.

Please see www.deloitte.com/us/about or a detailed description o the legal structure o Deloitte LLP and its subsidiaries. Certain services may

not be available to attest clients under the rules and regulations o public accounting.

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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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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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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Contacts

Andrew Freeman

Executive Director


+1 212 436 4676

[email protected]

Deron Weston

Principal


+1 404 631 3519

[email protected]

Omer Sohail

Principal


+1 214 840 7220

[email protected]

Leandro Dalle Mule

Senior Manager


+1 617 437 3449

[email protected]

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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Evaluating First Time Defaulters From the Inside Out

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