# Presentation: 487152

## Credit Scoring 101

- Federal Trade Commission
- July 22, 1999

## Topics

- 1. The nature of the credit decision
- 2.	Statistical vs. judgmental decisions
- 3.	Scoring system development
- 4.	Types of credit scoring systems
- 5.	Credit bureau scores
- 6.	Legal and policy issues

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## Never make predictions,especially about the future.

- Never make predictions,especially about the future.
*—**Casey Stengel*

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## Two types of credit decisions

- JUDGMENTAL

**STATISTICAL**

- ?

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## The credit decisionScoring vs. judgmental

- Both methods
- Assume that the future will resemble the past
- Compare applicants to past experience
- Aim to grant credit only to acceptable risks

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## The credit decisionScoring vs. judgmental (cont.)

- Added value of scoring
- Defines degree of credit risk for each applicant
- Ranks risk relative to other applicants
- Allows decisions based on degree of risk
- Enables tracking of performance over time
- Permits known and measurable adjustments
- Permits decision automation

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## Evaluating the credit applicant

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## Scorecard

- Own / Rent

- <5
- 12

- Prof
- 50

- <.5
-   2

- None
- 0

- Check
-  5

- <15
-  22

- 0
- 3

- <.5
-   0

- 0
- 5

- 0-15%
- 15

- No Rcrd
- 0

- Years at
- address

- Occupation

- Dept St /
- Major CC

- Bank
- reference

- Debt ratio

- No. of recent
- inquiries

- Years in file

- # Rev trades
- outstanding

- % Credit line
- utilization

- Worst
- reference

- Rent
- 15

- .5-2.49
- 10

- SemiPrf
- 44

- .5-1.49
-   8

- Dept-St
- 11

- Sav
-  10

- 15-25
-  15

- 1
- 11

- 1-2
-  5

- 1-2
- 12

- 16-30%
- 5

- Any Der.
- -29

- Other
- 10

- 2.5-6.49
- 15

- Mgr
- 31

- 1.5-2.49
-  19

- Maj-CC
- 16

- Ck&Sav
- 20

- 26-35
-  12

- 2
- 3

- 3-4
-  15

- 3-5
-  8

- 31-40%
- -3

- Any Slow
- -14

- NI
-  17

- 6.5-10.49
- 19

- >10.49
- 23

- NI
- 14

- Offc.
- 28

- 2.5-5.49
-  25

- Both
- 27

- Other
- 11

- 36-49
-  5

-  3
- -7

- 5-7
-  30

-   6+
- -4

-  41-50%
- -10

- 1Satis
- 17

- Bl.Col
- 25

- 5.5-12.49
-  30

- No answr
- 10

- NI
-  9

-  50+
-  0

-  4
- -7

-  8+
-  40

-  >50%
- -18

- 2Satis
- 24

- 3Satis
- 29

-  5-9
- -20

- No Rcrd
- 0

- NI
- 13

- NI
- 12

- Retired
- 31

- Other
- 22

- NI
-  27

- 12.5
- 39

- Retired
-  43

- NI
-  20

- Own
- 25

- Years on job

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## Information value

- “ NO INFORM ”

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## Developing population odds

- REJECTS
**3,000**

**8.7 / 1**

- 10,000 “through-the-door” Applicants

**GOODS**

**BADS**

**ODDS**

**24 / 1**

**16 / 1**

**GOODS**

**BADS**

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## Odds development

**Overall odds = population odds x information odds**

- 16 / 1 x 1 / 4 = 4 / 116 / 1 x 4 / 1 = 64 / 1

**AGE**

**% **
**GOOD**

**% **
**BAD**

**INFO**
**ODDS**

**<30**

**30's**

**40's**

**>50**

**10**

**20**

**30**

**40**

**100%**

**100%**

**40**

**30**

**20**

**10**

**1 / 4**

**2 / 3**

**3 / 2**

**4 / 1**

**POPULATION ODDS = 16 / 1**

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## Odds development

**POPULATION ODDS = 16 / 1**

**RES**

**% **
**GOOD**

**%**
** ****BAD**

**INFO**
**ODDS**

**Own**

**Rent**

**Other**

**60**

**30**

**10**

**30**

**60**

**10**

**2 / 1**

**1 / 2**

**1 / 1**

**100%**

**100%**

- 16 / 1 x (1 / 4 x 1 / 2) = 2 / 116 / 1 x (1 / 4 x 2 / 1) = 8 / 1

- 91821plm. ppt

## Odds development

**POPULATION ODDS = 16 / 1**

**RES**

**% **
**GOOD**

**%**
** ****BAD**

**INFO**
**ODDS**

**Own**

**Rent**

**Other**

**60**

**30**

**10**

**30**

**60**

**10**

**2 / 1**

**1 / 2**

**1 / 1**

**100%**

**100%**

- 16 / 1 x (1 / 4 x 1 / 2) = 2 / 116 / 1 x (4 / 1 x 2 / 1) = 128 / 1

- 16 / 1 x 1 / 4 = 4 / 116 / 1 x 4 / 1 = 64 / 1

**AGE**

**% **
**GOOD**

**% **
**BAD**

**INFO**
**ODDS**

**<30**

**30's**

**40's**

**>50**

**10**

**20**

**30**

**40**

**100%**

**100%**

**40**

**30**

**20**

**10**

**1 / 4**

**2 / 3**

**3 / 2**

**4 / 1**

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## Interrelationships

- Own / Rent

- Age

- Time at
- address

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## Interrelationships

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## Divergence

- Scoring System A

**Cutoff**

- Bads

- Goods

**10%**

**% APPLICANTS**

**35%**

**Cutoff**

**SCORE**

- Bads

- Goods

**20%**

**% APPLICANTS**

**35%**

- Scoring System B

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## Credit score = odds (risk)

- 80 / 1
- 20 / 1
- 5 / 1
- 1 / 1

- •
- •
- •

- •
- •
- •

- SCORE

- 240
- 200
- 160
- 100

- •
- •
- •

- •
- •
- •

- •
- •
- •

- •
- •
- •

- =
- =
- =
- =

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## IT’S BETTER TO COUNT, THAN TO GUESS!!!

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## Source

- Specific creditor
- Custom scorecards
- Other creditor / portfolio
- Borrowed scorecards
- Multiple creditors
- Pooled data scorecards
- Prior scorecard experience
- Launch scorecards

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## Sources of information

- Credit reports
- Application data
- Prior experience
- Demographics
- Billing file
- Deal terms

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## Decision areas

- Solicitations
- New applications
- Account management
- Credit line
- Authorization
- Collections
- Reissue
- Cross-sell
- Keep / sell

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## Performance definitions

- Delinquency ("Good-Bad")
- Bankruptcy
- Response
- Attrition/Prepayment
- Profitability
- Collections
- Revenue

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## Scoring in mortgage lending

- Scoring in mortgage lending

- “Generic” bureau scores
- Mortgage-specific bureau scores
- Comprehensive mortgage scores
- Differences from other types of credit
- Recommended usage

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## Credit Bureau Scores

- TYPES OF CHARACTERISTICS
- Previous credit performance
- Current level of indebtedness
- Amount of time credit has been in use
- Pursuit of new credit
- Types of credit available

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## Previous credit performance

- What types of information does this include?
- Trade lines
- Public records
- Collection segments
- What questions are answered by this information?
- Recency – How long ago was the last delinquency?
- Severity – What level of delinquency was reached?
- Prevalence – How many creditobligations have been delinquent?

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## Previous credit performanceExample information

- Previous credit performanceExample information

- Number of months since most recent 60 day or higher level of delinquency reached
- Highest level of delinquency reached in the last year
- Number of months since most recent derogatory public record

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## Previous credit performance

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## Current level of indebtednessExample information

- Average balance on revolving trade lines
- Ratio of revolving debt to total revolving limits
- Percentage outstanding on open installment loans

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## Current level of indebtedness

**HIGH**

**LOW**

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## Amount of time credit has been in use

**HIGH**

**LOW**

- Amount of time credit has been in use

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## Pursuit of new credit

**HIGH**

**LOW**

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## Types of credit available

**HIGH**

**LOW**

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## Score Date

- * * *

**14 day de-dupe**

- * *

**30 day buffer**

- 12* *months

***The credit bureau risk score models observe inquiries over a 12-month period. Auto- and mortgage-related inquiries that occur 30 days prior to scoring have no effect on the score. Outside this 30-day period, auto- and mortgage-related inquiries that occur within any 14-day period are treated as a single inquiry.***

-  Inquiry de-dupe logic

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## Inquiry de-dupe & buffer period

- (Ignore within 30 days)

- Counted as 1 Inq.
- (De-dupe within14 days)

- Counted as 1 Inq.

- TYPE

- NUMBER OF
- DAYS AGO

- Dept. Store
- Mortgage
- Mortgage
- Auto
- Auto
- Auto

- 68
- 65
- 56
- 17
- 9
- 8

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## Multiple scorecard design

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## Multiple scorecard design

**CREDIT BUREAU FILE**

- Previous serious delinquencyor derogatory public record?

**Based upon:**
- Public record
- Worst rating

**Based upon:**
- Thickness of file
- Age of file
- Recency of new openings

**Yes**

**No**

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## Multiple scorecard design opportunities

- Increased predictive power within each targeted population
- Exploit differences in predictive patterns
- Specialized risk assessment for important credit markets

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## Legal and Policy Issues

- Legal and Policy Issues

- Overt discrimination
- Disparate treatment
- Overrides
- Quality of assistance
- Disparate impact: myth vs. data

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## LMI StudyAcceptance ratesTotal scorecard

**CUTOFF  SCORE**

**180**

**190**

**200**

**210**

**220**

**230**

**240**

- LMI population
- Higher income population
- Total population

**90%**
**80%**
**70%**
**60%**
**50%**
**40%**
**30%**
**20%**
**10%**
**0%**

- LMI StudyAcceptance ratesTotal scorecard

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## LMI Study Odds vs. scoreTotal Scorecard

**SCORE**

**LN  (ODDS)**

**160**

**170**

**190**

**210**

**220**

**230**

**240**

**200**

**180**

- LMI Study Odds vs. scoreTotal Scorecard

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## LMI Study Population differences

- MODERATEDIFFERENCE

- Personal finance company reference
- Number of inquiries in last 6 months

- LITTLEDIFFERENCE

- Checking/ savings reference
- Net fraction revolving burden

- LARGEDIFFERENCE

- Residential status
- Occupation
- Worst rating

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## LMI Study Maintain bad rate; compare acceptance rates

- LMI Study Maintain bad rate; compare acceptance rates

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## LMI Study Maintain acceptance rate; compare bad rates

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## HMA Study Defined high minority areas (HMA)

- HMA Study Defined high minority areas (HMA)

- Census bureau data used
- HMA consumers who reside in zip codes with 70+% black and / or hispanic population
- These areas account for
- 8.3% of the US population
- 6.7% of all credit records

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## HMA Study Income is correlated with HMA

- $21,900

- HMA

- Total Population Minus HMA

- $33,700

- HMA Study Income is correlated with HMA

**Median Annual****Household Income**

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## HMA Study Comparison of score distributions

**650**

**Fair, Isaac Credit Bureau Score**

**HIGH**

**PERCENT**

**LOW**

**Total population minus HMA **
**High Minority Areas (HMA)**

- HMA Study Comparison of score distributions

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## HMA Study Comparison of odds versus score

**HIGH**

**Total minus HMA**
**HMA**

**Odds at each score are slightly lower for HMA as for rest of population**

**Fair, Isaac Credit Bureau Score**

**LN (ODDS)**

- HMA Study Comparison of odds versus score

**LOW**

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## HMA Study Population differences

- Number of recent inquiries
- Number of recently opened trades
- Number of finance company trades

- LITTLEDIFFERENCE

- Time since recent delinquency
- Previous minor delinquency
- Number of home loans

- HMA Study Population differences

- Previous serious delinquency
- Utilization
- Oldest date open
- Number of credit acc’ts available

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## HMA Study More predictive score for minorities?

**HIGH**

**Fair, Isaac CB score outperforms single “HMA” score on the high minority area population**

**Overall Score**
**HMA Score**

**% HMA Goods**

**% HMA BADS IDENIFIED**

**Fair, Isaac CB Score**

- HMA Study More predictive score for minorities?

**LOW**

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