Credit Card Scoring Systems

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Scoring SystemsChapter 16


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EXAMPLE: CREDIT CARD

APPLICATION

Chapter 16Scoring Systems 1


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Chapter 16Scoring Systems

EXAMPLE: CREDIT CARD

APPLICATION

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Description Mathematical methods (scoring systems) Customer selection Allocate resources among customers

Purposes

Replace individual judgment with a cheaper andmore reliable method

Augment individual judgment by variable

reduction


Introduction

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Typically the decision is either accept

or reject, in other words a 0 or a 1 Separate existing customers into two

groups: "good" and "bad

(Example: Customers who paid back aloan vs customers who defaulted on aloan)


Method

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Find variables associated with good/badresults

Determine a simple numerical score thatidentifies the risk (probability) of

good/bad results Determine a risk cut-off level thatmaximizes firm effectiveness

Customers over cut-off accepted, below

cut-off rejected


Method

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Customer solicitation Lead generation for cold calls, list generation

for mailingsreduces costs by eliminatingunlikely customers from list

Customer evaluation Credit granting, school admissions

Resource allocation to customers Live telephone call, automated call, letter,

Data reduction (Apgar, Apache medical

scores) Simplifying information


RelevanceUses of Scoring

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Types of companies that use scoring

Retail Banks Finance Houses

Loan approval for credit cards, auto loans, home loans,small business loans

Solicitation for products (pre-approved credit cards)

Credit limit settings and extensions Credit usage Customer retention Collection of bad debts

Merchant Banks Corporate bankruptcy prediction from financial ratios

Utility Companies Credit line establishment

Length of service provisionChapter 16Scoring Systems

Relevance - Breadth of Corporate Use

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IRS Income tax audits

Parole Boards Paroling prisoners

Mass Mail/Telemarketing

Retailers Target market identification (e.g., high incomes) Selecting solicitation targets (response rate prediction)

Insurance Auto/homewho to accept/reject, level of premium credit

score as a predictor of auto accidents Education Accept/rejecttoo good to go here financial aid as

enticement to attend


Relevance - Breadth of Corporate Use

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History of Scoring Systems

Developed in 1941 for use by HouseholdFinance Co. (HFC)

Acceptance by banks in the 1970s Profitability

Equal Credit Opportunity Act (ECOA) andRegulation B prohibited discrimination in lending

Discrimination could be proven statistically

Scoring was designed as a statistically sound,empirically based system of granting credit

Explosion in the use of scoring in the1980s/90s due to increased computationalability



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Many models derived "in-house U.S. firms

Fair, Isaac and Co.California MDSGeorgia Mathtec - New Jersey

European firms Scorelink Scorex Ltd. CCN Systems

Results Bank credit cards: average reduction in ratio of bad

debts/total portfolio of 34%, need fewer lenders Direct mail: cuts mailing costs 50% while cutting

response rate only 13%

The Market



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Example: Profit from good account, $1; loss from a bad

account, $9 Approve 100 accounts each with odds of 95%

good Profit = 95x$1 - 5x$9 = $50 Approve 100 accounts each with odds of 80%

good Profit = 80x$1 - 20x$9 = -$100 Approve accounts until Expected Profit = Expected Loss from marginal

account


Methods

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Example P= Odds of good account Expected Profit = Profit x P Expected Loss = Loss x (1-P) Profit x P = Loss x (1-P) Profit x P = Loss - (Loss x P) P = Loss / (Profit + Loss) P=9/(9+1)=90%

Conclusion: need accurate assessment of

"odds"


Methods

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Numerical Risk Score

Example: direct mail costs $0.45 perpiece if it lands in the trash and anaverage profit of $20 per positiveresponse, it would be profitable to send

mailings to those with a probability of 2.2%or higher of responding

%2.2)45.00.20(

45.

BadofCostGoodofProfit

BadofCost



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Data Collection:

Dependent Variable: Separate historicalresults into "good" and "bad" groups

(0,1) dependent variable

Independent Variables: Information from

appropriate sources (e.g., creditapplication, purchasing behavior) thatmay be associatedwith outcome

Expensive, time consuming in somecasesChapter 16Scoring Systems 14


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Usual procedure: divide all independent variables into (0,1)

variables

For example: If income < 25,000, then variable IN1 = 1, elseIN1 = 0

If 25,000 < income < 50,000, then variable IN2 = 1, else IN2= 0, etc.

Income Inc


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Modeling techniques that give "odds" of a

good/bad outcome Multiple regression Logistic regression - designed for (0,1) dependent

variable Discriminant analysis - develops variable weights

for the maximum separation of the means of thetwo groups Recursive partitioning - repeatedly splitting into

two groups as alike as possible in terms ofindependent variables, and as different as possiblein terms of the dependent variable

Nested regression or discriminant analysis - moreclosely examines those "on the bubble"


Models

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Example: Profit $1, Loss $9, so P = .90 Rule: accept all accounts with score >.90

Regression: Dependent variable: 1 if good, 0if badY = B0+B1X1+B2X2....40 + .20 Own Home - .75 Other+ .40 S+C w/bank +.25 S+C + .15 checking+ .15 (56+yrs old) + .10 (36-55) + .05 (


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Probability of good accountAnn Bob Craig Dave Eileen Frank1.30 .70 .85 .80 .80 -.20


Credit Card Account Modeling

Multiple Regression Model

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Paid = 1 * * * * * * *

Fitted Regression Line

Defaulted = 0 * ** * * * *Chapter 16Scoring Systems

Multiple Regression Fit of a PerfectData Set

LoanResult

20 25 30 35 40 45 50Age

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Paid = 1 * * * * * * *

Fitted Regression Line

Defaulted =0 * ** * * * *Chapter 16Scoring Systems

Multiple Regression Fit of a PerfectData Set

LoanResult

20 25 30 35 40 45 50Age

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Logistic Regression

Logisitic regressionfits the function:

Which becomes:

Determine the cutoffscore based on themonetaryrelationship between

good and badaccounts

)1(ln

odds

oddsscore

)1(

score

score

e

eodds

718.2e



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Scorecard Example

Calculate the cutoff score Assume that the probability of a good accountwould have to be 90% for approval

The cutoff score would be:

20.2)90.1(

90.lnscorecutoff



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Scorecard Example

Logistic regression gives the followingequation:

Multiply all values X 100 for simplicity

yrs)0.25(5to1010yrs)0.53(er)0.26(labor-er)0.25(manag

ed)0.33(retir5)0.20(26to3-5)0.15(36to556).5(age

ing)0.05(check-C)&(S0.85)0.05(other-home)own(3.18.0score



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Scorecard Example

Base a scorecard on the fitted equation: Everyone starts with 80 points

Residence Own Home+130

Other-5

BankAccounts Savings and Checking with bank+85Checking only-5

Age 56+

+50

36-55

+15

26-35

-20

Work Retired

+33

Manager

+25

Laborer

-26

Time on Job 10 yrs or more+53

5-10 yrs on job+25



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Scorecard Example A 65 year old retired homeowner with only

a checking account with the bank, whoworked for 8 years for his previousemployer would score:

Since 313>220, the loan would beapproved

313253350513080

(5to10yrs)retired56agecheckingownbase



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Other Scoring Models

Decision-Tree Score Cards Follow a path based on demographic

characteristics until a branch ends inacceptance or rejection



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Applicant

Own Home Rent Other than

rent or own

Probability of

good account

0.95 0.89 0.73

DeclineAcct w/ bank No Account

with bank

0.99 0.92

Accept

Recursive Partitioning



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Analyzes customer behaviorinstead of

demographic characteristics ExampleBad Debt Collection

Costs (GE Capital 1990): $12 billion portfolio $1 billion delinquent balances

$150 million collection efforts $400 million write-offs

Resources: Letters (many types) Interactive taped phone messages (2 levels of severity) Live phone calls from a collector Legal procedures


Behavioral Scoring

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Daily Volume: 50,000 taped calls 30,000 live calls

Need for strategy: Too expensive - actual costs and goodwill to

personally call each delinquent Customers require different amounts of prodding topay

Results: Scoring indicated that more customers should be

handled by "doing nothing Scoring reduced losses by $37 million/year, using

fewer resources and with more customer goodwill


Behavioral Scoring

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Problems with Scoring Systems

Good vs. Bad doesnt take into accountunderlying differences in customerprofitability

Screening bias

If certain demographics are not present in thecurrent customer base, theres no way tojudge them with a scoring system

Scoring systems are only valid as long asthe customer base remains the same Update every three to five years



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Implementation Problems Fairness

Scoring systems may lock out minorities Manual overrides (exceptions) may favor non-

minority customers

Impersonal decision making

Federal Reserve governor denied a Toys RUs credit card

Face Validity: Does the data makesense?

Misuse/nonuse of score cards



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Using SPSS for Logistic Regressionon the MBA S&L caseInitial screen:

Open file from CD-ROM, chapter16_mbas&l_case_SPSS_format

On menu: Analyze, Regression, Binary Logistic

In the logistic regression menu:

good is the dependent variable

Choose independent variables as you see fit

Under options the classification cut-off is set at 0.5. Insert a cut-off appropriate for the case data.


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Credit Card Scoring Systems