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Scoring Systems

Scoring Systems. Chapter 16. EXAMPLE: CREDIT CARD APPLICATION. Chapter 16 – Scoring Systems. 1. EXAMPLE: CREDIT CARD APPLICATION. Chapter 16 – Scoring Systems. 2. Introduction. Description Mathematical methods (scoring systems) Customer selection Allocate resources among customers

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Scoring Systems

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  1. Scoring Systems Chapter 16

  2. EXAMPLE: CREDIT CARD APPLICATION Chapter 16 – Scoring Systems 1

  3. EXAMPLE: CREDIT CARD APPLICATION Chapter 16 – Scoring Systems 2

  4. Introduction • 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 3

  5. Method • 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 4

  6. Method • 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 5

  7. Relevance – Uses of Scoring • 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 6

  8. Relevance - Breadth of Corporate Use • 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 7

  9. Relevance - Breadth of Corporate Use • 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 8

  10. History of Scoring Systems • Developed in 1941 for use by Household Finance Co. (HFC) • Acceptance by banks in the 1970’s • 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 1980’s/90’s due to increased computational ability Chapter 16 – Scoring Systems 9

  11. The Market • 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% Chapter 16 – Scoring Systems 10

  12. Methods • 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 11

  13. Methods • 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 12

  14. 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 Systems 13

  15. 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 Systems 14

  16. Data Collection: • 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. Chapter 16 – Scoring Systems 15

  17. Models • 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 16

  18. Credit Card Account Modeling Multiple Regression Model • 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 = 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 (<25) • + .15 Retired + .05 Mgr - .05 Laborer • + .10 (10+ yrs job) + .05 (5-10 yrs) Chapter 16 – Scoring Systems 17

  19. Credit Card Account Modeling Multiple Regression Model • Probability of good account • Ann Bob Craig Dave Eileen Frank • 1.30 .70 .85 .80 .80 -.20 Chapter 16 – Scoring Systems 18

  20. Multiple Regression Fit of a Perfect Data Set Paid = 1* * * * * * * Loan Result Fitted Regression Line Defaulted = 0* ** * * * * 20 25 30 35 40 45 50 Age Chapter 16 – Scoring Systems 19

  21. Multiple Regression Fit of a Perfect Data Set Paid = 1* * * * * * * Loan Result Fitted Regression Line Defaulted =0* ** * * * * 20 25 30 35 40 45 50 Age Chapter 16 – Scoring Systems 20

  22. Logisitic regression fits the function: Which becomes: Determine the cutoff score based on the monetary relationship between good and bad accounts Logistic Regression Chapter 16 – Scoring Systems 21

  23. 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 Systems 22

  24. Scorecard Example • Logistic regression gives the following equation: • Multiply all values X 100 for simplicity Chapter 16 – Scoring Systems 23

  25. Scorecard Example • Base a scorecard on the fitted equation: • Everyone starts with 80 points Chapter 16 – Scoring Systems 24

  26. 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 Systems 26

  27. 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 Systems 27

  28. Recursive Partitioning • Probability of good account Applicant 0.95 0.89 0.73 Own Home Rent Other than rent or own 0.99 0.92 No Account with bank Acct w/ bank Decline Accept Chapter 16 – Scoring Systems 27

  29. Behavioral Scoring • 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 28

  30. Behavioral Scoring • 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 29

  31. Problems with Scoring Systems • “Good” vs. “Bad” doesn’t take into account underlying differences in customer profitability • Screening bias • If certain demographics are not present in the current customer base, there’s 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 Systems 30

  32. 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 Systems 31

  33. 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 Systems 32

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