Credit History Impact on Personal Lines Loss Experience

Session CPP-49

James E. Monaghan

Thurs. March 9, 2000

CAS Ratemaking Seminar

Purpose of Study

• Much discussion over presence or lack of presence of correlation between credit history and future personal lines losses

• Purpose was to determine if this relationship exists

• Still much discussion over what defines correlation when outside statistical spectrum

Purpose of Study

• “Correlation” involves a large number of variables as independent variable with one apparent dependent variable, loss ratio

• Which variables from the credit file are predictive of future loss activity greater or less than average?

• What are the various strengths of each of these variables? (i.e., weights to apply)

Purpose of Study

• Are there other policy characteristics, either rated for or not rated for, which duplicate the impact of credit history on losses? (i.e., independence of credit)

• How much cross-dependency exists within a large number of underwriting or rating characteristics when measured against credit history?

Research Database

• All policies written as new business in policy year 1993

• Calendar/accident year premium and loss during time period 1/93 though 12/95

• All policy characteristics measured at time of initial writing

• Credit data recalled from bureau archives at time closest to original time of writing

• Compilation done during 1997; FCRA compliance issues

Current Delinquent Amounts

AmountPast Due

Earned Premium

Loss Ratio

Relative Loss Ratio

Less than $500

$ 345.7 72.6% 0.95

$500 or more $ 48.3 102.0% 1.34

Total $ 394.0 76.3% 1.00

Derogatory Public Records

Number of DPRs

Earned Premium

Loss Ratio

Relative Loss Ratio

None, or withLiability = $0

$ 362.9 74.0% 0.97

One or more,Liability > $0

$ 31.1 102.2% 1.34

Total $ 394.0 76.3% 1.00

Collection Records

Number ofCollections

Earned Premium

Loss Ratio

Relative Loss Ratio

None, or withLiability = $0

$ 371.7 74.4% 0.98

One or more,Liability > $0

$ 22.3 107.6% 1.41

Total $ 394.0 76.3% 1.00

Revolving AccountLeverage Ratio

Leverage Ratio

EarnedPremium

Loss Ratio

RelativeLoss Ratio

Under 10% $ 127.4 64.5% 0.84

11-60% $ 133.5 72.3% 1.95

60% + $ 104.1 90.1% 1.18

Age of Oldest Trade Line

Years sinceoldest tradeopened

EarnedPremium

Loss Ratio

RelativeLoss Ratio

0-6 years $ 91.6 87.8% 1.15

7-9 years $ 76.1 79.0% 1.04

10+ years $ 226.2 70.7% 0.93

Profile Groups

• For purposes of loss comparisons, all risks grouped into 4 mutually exclusive categories based on 7 credit record variables

• APD, DPR, collections, inquiries, leverage ratio, Age of oldest trade line, worst current trade line status are the 7 variables used to create the groups

Profile Groups

• Goal in Creating mutually exclusive groups:

• A) Large difference between each group and its neighbor in loss ratio

• B) Significant percentage of premium distribution in each group

Profile Group Performance

Earned Premium

Loss Ratio

RelativeLoss Ratio

PremiumDistribution

A $ 74.3 101.4% 1.33 18.8%

B $ 158.9 78.5% 1.03 40.3%

C $ 69.0 69.1% 0.91 17.5%

D $ 91.8 57.4% 0.75 23.2%

Total $ 394.0 76.3%

Multivariate: Driving Record

• All driving record types reduced to three groupings:– Clean in 3 years prior (includes 1 minor

moving violation)– One accident in 3 years prior (fault or non-

fault)– All other (by definition 2+ incidents in 3 years

prior of any kind)

Driving Record

CreditGrouping Clean in 3

One acc in 3

2+ inc in 3 Total

A $36.8 1.25

$10.9 1.36

$25.3 1.45

$73.1 1.34

B $83.6 0.94

$23.3 1.02

$50.3 1.19

$157.2 1.03

C $38.1 0.85

$11.6 0.90

$18.5 1.02

$68.3 0.90

D $53.6 0.69

$16.1 0.88

$21.1 0.81

$90.8 0.75

Total all Groups

$212.2 0.91

$62.0 1.02

$115.2 1.15 $389.4

Age of Driver 1

Age A B C D Total

Under 30 1.46 1.01 0.81 0.81 1.05

30-54 1.30 1.06 0.92 0.74 1.01

55 andover

1.47 1.00 0.97 0.80 0.96

Total 1.33 1.03 0.91 0.76 1.00

Classical Underwriting Profile

• Attempt to override all “stability” factors

• Group data by – driving record– marital status– home ownership– number of vehicles

• Is credit impact diminished or eliminated?

Classical Underwriting Profile

Credit History Grouping

Married, Mulitcar,homeowner, cleandriving record

Unmarried, singlecar, renter, drivingrecord activity

A 1.27 (1.32) 1.47 (1.28)

B 1.00 (1.04) 1.14 (0.99)

C 0.99 (1.03) 0.96 (0.83)

D 0.74 (0.77) 0.91 (0.79)

Total 0.96 1.15

Rating Territory

• Recent review of distribution by credit group in urban, suburban and rural groupings of territories

• Done for auto line of business in New York, Connecticut, Ohio

• Virtually no variation in distribution or loss performance relativities based on territory type

• New York City experienced greatest average premium decrease with implementation of rate factors based on groups (relative to suburban and rural areas of New York state)

Policyholder Retention

• In both current data and data from research database, customers with better bill paying histories have higher retention

• Less likely to shop: price elasticity

• Chicken and egg question: between retention, loss performance, and credit history

Homeowners Result Comparison

Line ofbusiness

A B C D

Home 1.74 1.04 0.85 0.74

Distribution 14.7% 34.5% 9.9% 40.9%

Auto 1.33 1.03 0.91 0.75

Distribution 18.9% 40.3% 17.5% 23.3%

Home: Amount Past Due

CategoryEarnedPremium

Loss Ratio

RelativeLoss Ratio

APD < $500 $ 113.2 60.4% 0.94

APD > $500 $ 6.8 124.9% 1.95

Home: Collection records

CategoryEarnedPremium

Loss Ratio

RelativeLoss Ratio

No CollectionRecords

$ 112.0 59.7% 0.93

1 Collection $ 5.2 125.3% 1.95

2+ Collections $ 2.9 124.9% 1.97

Home: Derogatory Public Records

CategoryEarnedPremium

Loss Ratio

RelativeLoss Ratio

No DPR $ 105.4 57.7% 0.90

1 DPR $ 8.0 99.3% 1.55

2 DPR $ 3.0 122.5% 1.91

3+ DPR $ 3.6 125.1% 1.95

Frequency and Severity

• Credit influence on auto losses is predominantly frequency on both high and low ends

• Credit influence on homeowners is frequency only at low end, both frequency AND severity at high end

• Poor credit history for homeowners is only severity impact, and it is large

Credit as a Rating Variable

• How to combine elements from a credit report (large number of “facts”)– Which to choose– How to bin or group each variable– How to evaluate each if score weights are used

Control of the Insured

• Rating variables are best not to be influenced by intentional insured behavior

• Credit history falls under this category

• To what extent: If pre-existing financial disincentives have not caused behavioral changes, will auto insurance price?

Stability vs. Responsiveness

• Some variables are fixed for specific time period: Derog public records, collection records

• Some are transient, potentially daily: leverage ratio, account status, etc.

• Rating plan should contain some balance of these two types

Regulatory Approval & Societal Issues

• White Paper: underwriting vs. rating

• Fairness issues of reordering, discrimination, erroneous data, “explainable events”

• Causality arguments

• Education: perception of use of credit (historically negative)