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Scoring Systems

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Title: Scoring Systems

1
Scoring Systems
• Chapter 16

2
EXAMPLE CREDIT CARD APPLICATION
Chapter 16 Scoring Systems
1

3
EXAMPLE CREDIT CARD APPLICATION
Chapter 16 Scoring Systems
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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
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5
Method
• Typically the decision is either accept or
reject, in other words a 0 or a 1
• Separate existing customers into two groups
• (Example Customers who paid back a loan vs
customers who defaulted on a loan)

Chapter 16 Scoring Systems
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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
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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
• Resource allocation to customers
• Live telephone call, automated call, letter,
• Data reduction (Apgar, Apache medical scores)
• Simplifying information

Chapter 16 Scoring Systems
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Relevance - Breadth of Corporate Use
• Types of companies that use scoring
• Retail Banks
• Finance Houses
• Loan approval for credit cards, auto loans, home
• Solicitation for products (pre-approved credit
cards)
• Credit limit settings and extensions
• Credit usage
• Customer retention
• Merchant Banks
• Corporate bankruptcy prediction from financial
ratios
• Utility Companies
• Credit line establishment
• Length of service provision

Chapter 16 Scoring Systems
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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
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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 Systems
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The Market
• Many models derived "in-house
• U.S. firms
• Fair, Isaac and Co. California
• MDS Georgia
• Mathtec - New Jersey
• European firms
• 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
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Methods
• Example
• Profit from good account, 1 loss from a bad
account, 9
• Approve 100 accounts each with odds of 95 good
• Profit 95x1 - 5x9 50
• Approve 100 accounts each with odds of 80 good
• Profit 80x1 - 20x9 -100
• Approve accounts until
• Expected Profit Expected Loss from marginal
account

Chapter 16 Scoring Systems
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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)
• P9/(91)90
• Conclusion need accurate assessment of "odds"

Chapter 16 Scoring Systems
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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
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Data Collection
• Dependent Variable Separate historical results
• (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
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Data Collection
• Usual procedure divide all independent variables
into (0,1) variables
• For example If income IN1 1, else IN1 0
• If 25,000 1, else IN2 0, etc.

Chapter 16 Scoring Systems
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Models
• Modeling techniques that give "odds" of a
• 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
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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
• Y B0 B1X1 B2X2...
•
• .40 .20 Own Home - .75 Other
• .40 SC w/bank .25 SC .15 checking
• .15 (56yrs old) .10 (36-55) .05 (
• .15 Retired .05 Mgr - .05 Laborer
• .10 (10 yrs job) .05 (5-10 yrs)

Chapter 16 Scoring Systems
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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
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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
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Multiple Regression Fit of a Perfect Data Set
Paid 1

Fitted Regression Line
Loan Result
Defaulted 0
20 25 30 35 40 45 50 Age
Chapter 16 Scoring Systems
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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 Systems
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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
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Scorecard Example
• Logistic regression gives the following
equation
• Multiply all values X 100 for simplicity

Chapter 16 Scoring Systems
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Scorecard Example
• Base a scorecard on the fitted equation
• Everyone starts with 80 points

Chapter 16 Scoring Systems
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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 313220, the loan would be approved

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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
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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
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Behavioral Scoring
• Analyzes customer behavior instead of demographic
characteristics
• 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
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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
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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 Systems
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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 R Us
credit card
• Face Validity Does the data make sense?
• Misuse/nonuse of score cards

Chapter 16 Scoring Systems
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Using SPSS for Logistic Regression on the MBA
SL case
Initial screen Open file from CD-ROM,