Credit-Based Insurance Scores: Impacts on Consumers of

CREDIT-BASED INSURANCE SCORES:

IMPACTS ON CONSUMERS OF AUTOMOBILE INSURANCE

Jesse LearyFederal Trade Commission

Any views expressed are not those of the Federal Trade Commission or any individual Commissioner.

FACT Act of 2003 – Required Study

• Background on credit scores and credit-based insurance scores (“state of the world”)

• Study of effects of credit scores and credit-based insurance scores on:– Price and availability of credit and insurance products– Negative or differential treatment of protected classes under the

ECOA (and other defined groups)

• FTC Study on automobile insurance released in July 2007

• Federal Reserve Board study on credit scores and credit markets released in August 2007

• Still to come: FTC study of homeowners insurance

Negative or Differential Treatment (A)

Sec 215 (a)(2): a study of: “the statistical relationship, utilizing a multivariate analysis that controls for prohibited factors under the (ECOA) and other known risk factors, between … credit based insurance scores and the quantifiable risks and actual losses experienced by businesses;”

Sec. 215 (a)(3): a study of “the extent to which, if any, the use of … credit-based insurance scores impact on the availability and affordability of … insurance to the extent information is currently available or is available through proxies, by geography, income, ethnicity, race, color, religion, national origin, age, sex, marital status, and creed, including the extent to which the consideration or lack of consideration of certain factors by credit scoring systems could result in negative or differential treatment of protected classes under the (ECOA), and the extent to which, if any, the use of underwriting systems relying on these models could achieve comparable results through the use of factors with less negative impact;”

Negative or Differential Treatment (B)

Data

• Policy data – Subset of the “EPIC Database”– 5 firms, ~27% of the market– 1.4MM policies, 1.8MM earned car years– Analysis subset: 275K policies, 400K earned car years– Policy data

• Claims• Underwriting and rating variables

– ChoicePoint Attract credit score

• Race and Ethnicity Data – 3 Sources– Social Security Administration

• Pre-1981: Black/White/Other– Census (block level)– Hispanic surname match

Frequency and Severity of Claims by Credit-Based Insurance Score

Frequency of Claims

Average Severity of Claims

Property Damage Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores

10HighestScores

Deciles of ChoicePointCredit-Based Insurance Score

Relative Frequency or Severity

Bodily InjuryLiability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores

10HighestScores



CollisionCoverage

0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores

10HighestScores



ComprehensiveCoverage

0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores

10HighestScores


Relative Frequencyor Severity

0%

5%

10%

15%

20%

25%

30%

1LowestScores

10HighestScoresDeciles of ChoicePoint Credit-Based Insurance Scores

Percent

African AmericansHispanicsAsiansNon-Hispanic Whites

Equal Distribution Line

Distribution of Scores by Race and Ethnicity

0%

5%

10%

15%

20%

25%

30%

1LowestScores

10HighestScores

Deciles of ChoicePointCredit-Based Insurance Scores

Percent

Low-to-Moderate IncomeMiddleHigh


Distribution of Scores by Income

Distribution of Scores by Race and Ethnicity Controlling for Age, Gender, and Neighborhood Income

With ControlsWithout Controls

Non-Hispanic Whites

0%

5%

10%

15%

20%

25%

30%

1LowestScores


Percent African Americans

0%

5%

10%

15%

20%

25%

30%

1LowestScores


Percent

Hispanics

0%

5%

10%

15%

20%

25%

30%

1LowestScores


Percent Asians

0%

5%

10%

15%

20%

25%

30%

1LowestScores


Percent

10HighestScores

10HighestScores

10HighestScores

10HighestScores

Effect of Scores on Predicted Pure Premium By Race and Ethnicity

Non-Hispanic Whites

0

3

6

9

12

15

-30%-20%-10% 0% 10% 20% 30% 40% 50% 60%Percent Change

% Share of Group

Percent with Decrease: 62%

Percent w ith Increase: 38%

African Americans

0

3

6

9

12

15

-30% -20%-10% 0% 10% 20% 30% 40% 50% 60%Percent Change

% Share of Group

Percent w ith Decrease: 36%

Percent withIncrease: 64%

Hispanics

0

3

6

9

12

15

-30% -20%-10% 0% 10% 20% 30% 40% 50% 60%Percent Change

% Share of Group



Asians

0

3

6

9

12

15

-30% -20%-10% 0% 10% 20% 30% 40% 50% 60%Percent Change

% Share of Group



Estimated Relative Pure Premium by Race and Ethnicity

Property Damage Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores


Relative Claims


0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores


Relative Claims

CollisionCoverage

0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores


Relative Claims


0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores


Relative Claims

Non-Hispanic WhitesAfrican AmericansHispanicsAsians

10HighestScores

10HighestScores

10HighestScores

10HighestScores

Estimated Relative Pure Premium by Neighborhood Income

Property DamageLiability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores

10HighestScores


Relative Claims Bodily Injury

Liability Coverage

0.7

1.0

1.3

1.6

1.9

2.2

2.5

2.8

1LowestScores

10HighestScores


Relative Claims

CollisionCoverage

0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores

10HighestScores


Relative Claims


0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores

10HighestScores


Relative Claims

Low-to-Moderate IncomeMiddle IncomeHigh Income

Estimated Relative Pure Premium With Controls for Race, Ethnicity, and Income


0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores

10HighestScores


Relative Claims


0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores

10HighestScores


Relative Claims

CollisionCoverage

0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores

10HighestScores


Relative Claims


0.8

1.2

1.6

2.0

2.4

2.8

1LowestScores

10HighestScores


Relative Claims

Without Race, Ethnicity and Neighborhood Income ControlsWith Race, Ethnicity and Neighborhood Income Controls

Effects of Scores on Predicted Pure Premium With Controls for Race, Ethnicity, and Income

Average Score Effect From Model Without Race, Ethnicity, and Income

Controls

Average Score Effect from Model With Race,

Ethnicity, and Income Controls

(a) (b)

African Americans 10.0% 8.9%

Hispanics 4.2% 3.5%

Asians - 4.9% -4.8%

Non-Hispanic Whites - 1.6% -1.4%

FTC Score-Building - Data

• Policy database with race, ethnicity, income

• ChoicePoint credit-history variables– Have for all records except “no-hits”– 180 variables designed to capture information in consumer credit

reports• Various delinquency measures• Public records• Inquiries• Length of history• etc.

– Used by ChoicePoint in their model building– Not all variables appear in a ChoicePoint model– Proprietary

FTC Score-Building

• Step 1: Tweedie GLM of pure premium on “usual suspects” risk variables. Use predicted pure premium to create adjusted pure premium.

• Step 2: Bin credit history variables using a mechanical (non- judgmental) procedure.

• Step 3: Forward-selection OLS with adjusted total claims as dependent variable and 180 binned credit history variables as candidate explanatory variables.

• Step 4: Tweedie GLM of pure premiums on “usual suspects” risk variables and “winning” credit history variables. Use estimated parameters on credit history variables to create FTC scorecard.

FTC Baseline Scoring Model


0.8

1.2

1.6

2.0

2.4

2.8

3.2

1LowestScores

10HighestScores

Deciles of FTCCredit-Based Insurance Scores

Relative Claims

CollisionCoverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

1LowestScores

10HighestScores


Relative Claims


0.8

1.2

1.6

2.0

2.4

2.8

3.2

1LowestScores

10HighestScores


Relative Claims

Bodily Injury Liability Coverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

3.6

4.0

1LowestScores

10HighestScores

Deciles of FTC Credit-Based Insurance Scores

Relative Claims

Within Sample with ControlsWithin Sample without ControlsOut of Sample without Controls

Distribution of FTC Baseline-Model Scores by Race and Ethnicity

0%

5%

10%

15%

20%

25%

30%

1LowestScores

10HighestScores

Deciles of FTC Credit-BasedInsurance Scores

Percent African AmericansHispanicsAsiansNon-Hispanic Whites


FTC Scoring Models Built Controlling for Race, Ethnicity, and Income


0.8

1.2

1.6

2.0

2.4

2.8

3.2

1LowestScores

10HighestScores



Liability Coverage

0.8

1.2

1.62.0

2.4

2.8

3.2

3.6

4.0

1LowestScores

10HighesScores


Relative Claims

CollisionCoverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

1LowestScores

10HighestScores


Relative Claims


0.8

1.2

1.6

2.0

2.4

2.8

3.2

1LowestScores

10HighestScores


Relative Claims

FTC Baseline ModelModel Built with ControlsModel Built with Non-Hispanic Whites Only

Distribution of FTC Scores by Race and Ethnicity (A)

Non-Hispanic Whites

0%

5%

10%

15%

20%

25%

30%

1LowestScores



0%

5%

10%

15%

20%

25%

30%

1LowestScores


Percent

Hispanics

0%

5%

10%

15%

20%

25%

30%

1LowestScores


Percent Asians

0%

5%

10%

15%

20%

25%

30%

1LowestScores


Percent

FTC Baseline ModelModel Built with ControlsModel Built with Non-Hispanic Whites Only

10HighestScores

10HighestScores

10HighestScores

10HighestScores

Distribution of FTC Scores by Race and Ethnicity (B)

Non-Hispanic Whites

0%

5%

10%

15%

20%

25%

30%

1LowestScores



0%

5%

10%

15%

20%

25%

30%

1LowestScores


Percent

Hispanics

0%

5%

10%

15%

20%

25%

30%

1LowestScores


Percent Asians

0%

5%

10%

15%

20%

25%

30%

1LowestScores

Deciles of FTC Credit-Base Insurance Scores

Percent

FTC Baseline Model“Discounted Predictiveness” Model

10HighestScores

10HighestScores

10HighestScores

10HighestScores

FTC "Discounted Predictiveness" Model


0.8

1.2

1.6

2.0

2.4

2.8

3.2

1LowestScores

10HighestScores



Liability Coverage

0.8

1.2

1.62.0

2.4

2.8

3.2

3.6

4.0

1LowestScores

10HighesScores


Relative Claims

CollisionCoverage

0.8

1.2

1.6

2.0

2.4

2.8

3.2

1LowestScores

10HighestScores


Relative Claims


0.8

1.2

1.6

2.0

2.4

2.8

3.2

1LowestScores

10HighestScores


Relative Claims

FTC Baseline Model“Discounted Predictiveness” Model

Conclusions and Next Steps

• Scores predict risk– Lowest decile 1.7 to over 2 times riskier than highest

• Scores differ across racial and ethnic groups– Using scores raises average predicted pure premiums of African

Americans by 10% and Hispanics by 4.2%.

• Little of the relationship between scores and claims comes from the relationship between scores and race/ethnicity (the “proxy effect”).

• We could not develop an effective scoring model with smaller differences across groups.

• Up next: Homeowners!

Documents

Credit-Based Insurance Scores: Impacts on Consumers of