Scoring Systems - PowerPoint PPT Presentation

Loading...

PPT – Scoring Systems PowerPoint presentation | free to download - id: 1f3ab-ZGVmM



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Scoring Systems

Description:

Loan approval for credit cards, auto loans, home loans, small business loans ... Bank credit cards: average reduction in ratio of bad debts/total portfolio of 34 ... – PowerPoint PPT presentation

Number of Views:100
Avg rating:3.0/5.0
Slides: 34
Provided by: joanneliza
Learn more at: http://www.swlearning.com
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

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
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 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
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 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
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)
  • P9/(91)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 IN1 1, else IN1 0
  • If 25,000 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 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
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

Fitted Regression Line
Loan Result
Defaulted 0
20 25 30 35 40 45 50 Age
Chapter 16 Scoring Systems
20
22
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
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 313220, 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 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
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
SL case
Initial screen Open file from CD-ROM,
chapter16_mbasl_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
About PowerShow.com