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# ch16.ppt

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# ch16.ppt

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### ch16.ppt

1. 1. Scoring Systems Chapter 16
2. 2. EXAMPLE: CREDIT CARD APPLICATION Chapter 16 – Scoring Systems 1
3. 3. Chapter 16 – Scoring Systems EXAMPLE: CREDIT CARD APPLICATION 2
4. 4. • 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
5. 5. • 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
6. 6. • 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
7. 7. • 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
8. 8. • 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
9. 9. • 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
10. 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. 11. • 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 Systems 10
12. 12. • 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
13. 13. • 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
14. 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 % 2 . 2 ) 45 . 00 . 20 ( 45 . Bad of Cost Good of Profit Bad of Cost     Chapter 16 – Scoring Systems 13
15. 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. 16. • 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. Income Inc<25 25<Inc<50 Inc>50 26,555 0 1 0 33,456 0 1 0 113,000 0 0 1 90,000 0 0 1 15,000 1 0 0 12,000 1 0 0 Chapter 16 – Scoring Systems Data Collection: 15
17. 17. • 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
18. 18. • 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 Credit Card Account Modeling Multiple Regression Model 17
19. 19. • Probability of good account Ann Bob Craig Dave Eileen Frank 1.30 .70 .85 .80 .80 -.20 Chapter 16 – Scoring Systems Credit Card Account Modeling Multiple Regression Model 18
20. 20. Paid = 1 * * * * * * * Fitted Regression Line Defaulted = 0 * ** * * * * Chapter 16 – Scoring Systems Multiple Regression Fit of a Perfect Data Set Loan Result 20 25 30 35 40 45 50 Age 19
21. 21. Paid = 1 * * * * * * * Fitted Regression Line Defaulted =0 * ** * * * * Chapter 16 – Scoring Systems Multiple Regression Fit of a Perfect Data Set Loan Result 20 25 30 35 40 45 50 Age 20
22. 22. Logistic Regression • Logisitic regression fits the function: • Which becomes: – Determine the cutoff score based on the monetary relationship between good and bad accounts         ) 1 ( ln odds odds score ) 1 (   score score e e odds 718 . 2  e Chapter 16 – Scoring Systems 21
23. 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: 20 . 2 ) 90 . 1 ( 90 . ln score cutoff          Chapter 16 – Scoring Systems 22
24. 24. Scorecard Example • Logistic regression gives the following equation: • Multiply all values X 100 for simplicity yrs) 0.25(5to10 10yrs) 0.53( er) 0.26(labor - er) 0.25(manag ed) 0.33(retir 5) 0.20(26to3 - 5) 0.15(36to5 56) .5(age ing) 0.05(check - C) & (S 0.85 ) 0.05(other - home) own ( 3 . 1 8 . 0 score            Chapter 16 – Scoring Systems 23
25. 25. Scorecard Example • Base a scorecard on the fitted equation: – Everyone starts with 80 points Residence Own Home +130 Other -5 Bank Accounts Savings and Checking with bank +85 Checking 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 Chapter 16 – Scoring Systems 24
26. 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 313 25 33 50 5 130 80 (5to10yrs) retired 56 age checking own base             Chapter 16 – Scoring Systems 26
27. 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. 28. Applicant Own Home Rent Other than rent or own • Probability of good account 0.95 0.89 0.73 Decline Acct w/ bank No Account with bank 0.99 0.92 Accept Recursive Partitioning Chapter 16 – Scoring Systems 27
29. 29. • 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
30. 30. • 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
31. 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. 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. 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