Scoring Systems Chapter 16

EXAMPLE: CREDIT CARD APPLICATION Chapter 16 – Scoring Systems1

EXAMPLE: CREDIT CARD APPLICATION 2

Description Mathematical methods (scoring systems) Customer selection Allocate resources among customers Purposes Replace individual judgment with a cheaper and more reliable method Augment individual judgment by variable reduction Chapter 16 – Scoring Systems Introduction 3

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 a loan vs customers who defaulted on a loan) Chapter 16 – Scoring Systems Method 4

Find variables associated with good/bad results Determine a simple numerical score that identifies the risk (probability) of good/bad results Determine a risk cut-off level that maximizes firm effectiveness Customers over cut-off accepted, below cut-off rejected Chapter 16 – Scoring Systems Method 5

Customer solicitation Lead generation for cold calls, list generation for mailings – reduces costs by eliminating unlikely 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 Chapter 16 – Scoring Systems Relevance – Uses of Scoring 6

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 provision Chapter 16 – Scoring Systems Relevance - Breadth of Corporate Use 7

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/home – who to accept/reject, level of premium credit score as a predictor of auto accidents Education Accept/reject – too good to go here financial aid as enticement to attend Chapter 16 – Scoring Systems Relevance - Breadth of Corporate Use 8

History of Scoring Systems Developed in 1941 for use by Household Finance Co. (HFC) Acceptance by banks in the 1970s –Profitability –Equal Credit Opportunity Act (ECOA) and Regulation 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 the 1980s/90s due to increased computational ability Chapter 16 – Scoring Systems9

Many models derived "in-house U.S. firms Fair, Isaac and Co. – California MDS – Georgia 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 Chapter 16 – Scoring Systems10

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 Chapter 16 – Scoring Systems Methods 11

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" Chapter 16 – Scoring Systems Methods 12

Numerical Risk Score Example: direct mail costs $0.45 per piece if it lands in the trash and an average profit of $20 per positive response, it would be profitable to send mailings to those with a probability of 2.2% or higher of responding Chapter 16 – Scoring Systems13

Data Collection: Dependent Variable: Separate historical results into "good" and "bad" groups –(0,1) dependent variable Independent Variables: Information from appropriate sources (e.g., credit application, purchasing behavior) that may be associated with outcome Expensive, time consuming in some cases Chapter 16 – Scoring Systems14

Usual procedure: divide all independent variables into (0,1) variables For example: If income < 25,000, then variable IN1 = 1, else IN1 = 0 If 25,000 < income < 50,000, then variable IN2 = 1, else IN2 = 0, etc. IncomeInc<2525<Inc<50Inc>50 26, , , , , , Chapter 16 – Scoring Systems Data Collection: 15

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 the two groups Recursive partitioning - repeatedly splitting into two groups as alike as possible in terms of independent variables, and as different as possible in terms of the dependent variable Nested regression or discriminant analysis - more closely examines those "on the bubble" Chapter 16 – Scoring Systems Models 16

Example: Profit $1, Loss $9, so P =.90 Rule: accept all accounts with score >.90 Regression: Dependent variable: 1 if good, 0 if bad Y = B 0 +B 1 X 1 +B 2 X Own Home -.75 Other +.40 S+C w/bank +.25 S+C +.15 checking +.15 (56+yrs old) +.10 (36-55) +.05 (<25) +.15 Retired +.05 Mgr -.05 Laborer +.10 (10+ yrs job) +.05 (5-10 yrs) Chapter 16 – Scoring Systems Credit Card Account Modeling Multiple Regression Model 17

Probability of good account AnnBobCraig Dave Eileen Frank Chapter 16 – Scoring Systems Credit Card Account Modeling Multiple Regression Model 18

Paid = 1 * * * * * * * Fitted Regression Line Defaulted = 0 * ** * * * * Chapter 16 – Scoring Systems Multiple Regression Fit of a Perfect Data Set Loan Result Age 19

Paid = 1 * * * * * * * Fitted Regression Line Defaulted =0 * ** * * * * Chapter 16 – Scoring Systems Multiple Regression Fit of a Perfect Data Set Loan Result Age 20

Logistic Regression Logisitic regression fits the function: Which becomes: –Determine the cutoff score based on the monetary relationship between good and bad accounts Chapter 16 – Scoring Systems21

Scorecard Example Calculate the cutoff score –Assume that the probability of a good account would have to be 90% for approval –The cutoff score would be: Chapter 16 – Scoring Systems22

Scorecard Example Logistic regression gives the following equation: Multiply all values X 100 for simplicity Chapter 16 – Scoring Systems23

Scorecard Example Base a scorecard on the fitted equation: –Everyone starts with 80 points ResidenceOwn Home +130 Other -5 Bank Accounts Savings and Checking with bank +85 Checking only -5 Age WorkRetired +33 Manager +25 Laborer -26 Time on Job10 yrs or more yrs on job +25 Chapter 16 – Scoring Systems24

Scorecard Example A 65 year old retired homeowner with only a checking account with the bank, who worked for 8 years for his previous employer would score: Since 313>220, the loan would be approved Chapter 16 – Scoring Systems26

Other Scoring Models Decision-Tree Score Cards –Follow a path based on demographic characteristics until a branch ends in acceptance or rejection Chapter 16 – Scoring Systems27

Applicant Own HomeRentOther than rent or own Probability of good account Decline Acct w/ bank No Account with bank Accept Recursive Partitioning Chapter 16 – Scoring Systems27

Analyzes customer behavior instead of demographic characteristics Example – Bad 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 Chapter 16 – Scoring Systems Behavioral Scoring 28

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 to pay 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 Chapter 16 – Scoring Systems Behavioral Scoring 29

Problems with Scoring Systems Good vs. Bad doesnt take into account underlying differences in customer profitability Screening bias –If certain demographics are not present in the current customer base, theres no way to judge them with a scoring system Scoring systems are only valid as long as the customer base remains the same –Update every three to five years Chapter 16 – Scoring Systems30

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 R Us credit card Face Validity: Does the data make sense? Misuse/nonuse of score cards Chapter 16 – Scoring Systems31

Using SPSS for Logistic Regression on the MBA S&L case Initial 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. Chapter 16 – Scoring Systems32