Applications of Credit in Life Insurance

Transcription

1 Applications of Credit in Life Insurance Southeastern Actuaries Conference Derek Kueker, FSA MAAA June 25,

2 Proprietary & Confidential All of the information contained in this document is proprietary and confidential and may not be disclosed to any third party without the express written consent of RGA.

3 Agenda Introduction Model Creation & Validation Insured Lives Validation TrueRisk Life Deliverables Applications within Life Insurance 3

4 Introduction

5 Introduction Purpose RGA & TransUnion partnered together to better understand the value of credit data to life insurers and potential applications Background Credit-Based Insurance Scores (CBIS) used in P&C since the 1990 s Wide adoption in pricing & underwriting for auto and home insurance Predictive models built and validated using de-personalized credit data Goal of the Model To predict mortality The model is also extremely good at predicting lapses 5

6 Credit Reporting Process Data flow from the consumer transactions / behaviors to the credit report Consumer Collection Agencies Courts Lender / Creditor #1 Lender / Creditor #2.. Lender / Creditor #9 Utilities Etc. TransUnion Consolidates data, builds models Comprehensive reports on individuals (Scores, Attributes or Full file) Consumers Landlords P&C Insurers Life Insurers Lenders Utilities Collection Agencies Employers (new hires) 6

7 Credit Report Contents Demographics What is on a Credit Report Name, Age, SSN, DOB, Phone Trades (Loans) Credit Cards, Mortgages, Auto Loans, Personal Loans, Home Equity Loans, Other Debt Hard Inquiries Consumer-initiated applications for credit What is NOT on a Credit Report Specific Credit Card Purchase Transactions IRS Data Income, Race, etc. Checking/Savings Account Data Public Records Bankruptcies, Tax Liens, Civil Judgments 7

8 Introduction Credit Regulations and Insurance Adoption Federal Statute Fair Credit Reporting Act (FCRA) Section 604 specifies permissible purposes for use of consumer reports to a person which it has reason to believe intends to use the information in connection with the underwriting of insurance involving the consumer Thus, consumer reports may be used in connection with the underwriting of life insurance. State Regulations 50% of states adopt Model Act for P&C; sensible restrictions on credit variables Most other states propose their own rules for credit use in rating/underwriting P&C Insurers 99% of insurers use credit in connection with underwriting Credit-Based Insurance Scores (CBIS) in use since the 1990 s Life Insurers Underwriters are currently using credit reports Early adopters are using in connection with underwriting Used in direct marketing by several life carriers 8

9 Model Creation and Validation

10 Model Creation Building the Model Starting Data Variable Selection Model Process External Validation of Model TrueRisk Life Score Built the model on 44 million lives and over 3 million deaths Started with over 800 variables offering features of individual s credit history Selected variables that were: Most predictive of the outcome Stable over time Non-gameable Not too correlated with the other variables Binary Logistic Regression Model validated internally using an additional 30 million lives Age, Gender and Region used as control variables Tested model using traditional mortality and lapse studies Used a random holdout dataset of another 18 million lives TrueRisk Life presented as a score from: 1 to 100 Low Risk High Risk Data comes from de-personalized 1998 credit archive (90% of the US population) Model calibrated to actual deaths occurring over a 12-year period TransUnion & RGA built and tested TrueRisk Life on 92 million individuals 10

11 Model Creation Credit Report Variables Scores Credit Seeking Activity Derogatory Credit Information Credit Tenure Credit Usage New trades Inquiries Recency & Frequency Severity Recency Count Amount File thickness (e.g. number of active trades) Longevity (e.g. months since oldest trade) Utilization percentage Recency of use Usage patterns The attributes encompass the breadth of the data with the most emphasis on long-run behavior rather than short-term patterns 11

12 Model Validation Population Study Overall Mortality Details Mortality study performed on holdout sample of 18 million lives using a 1998 TransUnion archive and studying the lives during Score buckets are set to be uniform across the population Study shows 5 times segmentation ( compared to 1-5) SSMDF used as source of deaths; used population mortality tables 12

13 Model Validation Population Study Mortality by Age Group Mortality by Duration 13

14 Insured Lives Validation

15 Model Validation Insured Lives Study Distribution of Insureds (Compared to Population) Details of the Study Important to test the value of TRL on an insured block of business Business Studied: Full UW (term, UL, VUL) and small face WL Study Period: Mortality and Lapse result studied on a count basis Relative mortality and relative lapse results reported 15

16 Model Validation Insured Lives Study Fully Underwritten Mortality Study Mortality of the group is 2.6 times higher than the 1-10 group Term, UL & VUL; Face Amounts $100,000; Issue Ages < 70 16

17 Model Validation Insured Lives Study Fully Underwritten Mortality Study Segmentation exists within risk classes; Mortality for worst TRL scores (71-100) are about double that of best risks (1-10); Non-smokers are shown, but results are similar for smokers. Term, UL & VUL; Face Amounts $100,000; Issue Ages < 70 17

18 Model Validation Insured Lives Study Fully Underwritten Lapse Study Details Overall Lapse Results (Durations 1-2) Term, UL and VUL Face amounts $100k Issue Ages < 70 Results Overall Lapse Results (Durations 3+) Lapse rate of group is 6 times higher than 1-10 group in durations 1-2 Continued segmentation seen in later durations, but less dramatic Similar results seen when looking at the curves by issue age band 18

19 Model Validation Insured Lives Study Fully Underwritten Lapse Study Segmentation of about 6 times seen in first two durations within given risk class Non-smokers are shown, but results are similar for smokers Term, UL & VUL; Face Amounts $100,000; Issue Ages < 70 19

20 Model Validation Insured Lives Study Small Face Whole Life Mortality Study Overall Mortality (Issue Age < 70) Results Mortality about 6 times higher for worst scores Segmentation at higher scores for this business 14% of exposure & 29% of claims have a score > 95 > 10% of the claims have a score of 100 Value also seen beyond issue age 70 Includes Whole Life products < $100k face; most of this business is under $25k-$50k; Issue ages < 70; Scores above 90 are further split out 20

21 Model Validation Insured Lives Study Small Face Whole Life Lapse Study Details Overall Lapse Results (Durations 1-2) Includes Whole Life products Face amounts < $100k; most of this business is under $25k-$50k Issue Ages < 70 Results Overall Lapse Results (Durations 3+) Significantly higher lapse rates at the higher scores Raw lapse rates are much lower for durations 3+, but there is little segmentation by score 21

22 Model Validation Insured Lives Study Comparison of Fully Underwritten to Small Face Whole Life Details Term, UL and VUL; Face amounts $100k Whole Life Products < $100k face; most of this business is under $25k-$50k Issue Ages < 70 Mortality Relative to Total Fully Underwritten Term, UL & VUL $100k TrueRisk Life Score Group FUW Term, UL & VUL ( $100k) Small Face WL (mostly $25k-$50k) % 275% % 294% % 688% Total 100% 453% Results TRL Score alone is not fully indicative of a certain level of mortality Higher mortality for same TRL scores depending on target market, distribution, underwriting, etc. Exposure is much more heavily weighted to lower TRL scores for FUW business TrueRisk Life Score Group Exposure Count Distribution FUW Term, UL & VUL ( $100k) Small Face WL (mostly $25k-$50k) % 22% % 33% % 45% Total 100% 100% 22

23 TrueRisk Life Deliverables

24 TrueRisk Life Deliverables TrueRisk Life Score Adverse Action Codes Credit Report 24

25 Applications within Life Insurance

26 Applications within Life Insurance Dynamic Underwriting Further Risk Segmentation within a Fully Underwritten Environment Further Risk Segmentation within a Simplified Issue Environment Batch segmentation ( pre-approval ) for new firm life offers Cross-sell or up-sell existing customers Inforce Policy Management (lapse & mortality) 26

27 Dynamic Underwriting Example Application Full Application with Tele-Interview & Drill Downs Low TRL Scores Initial Screen TrueRisk Life Score High TRL Scores Issue Age & Face Amount Limitations N Intermediate TRL Scores Y Gather 3 rd Party Data Meets Req s N Y Audit Underwrite & Make Offer without additional testing Apply Full Underwriting Order Additional Req s 27

28 Simplified Issue Example Application Simplified Issue Application with Drill Downs Gather 3 rd Party Data Meets Req s N Y Low TRL Scores Intermediate TRL Scores High TRL Scores Offer Preferred SI Offer Standard SI Refer to Underwriter 28

29 Applications of Credit in Life Insurance Southeastern Actuaries Conference Derek Kueker, FSA MAAA June 25,