Disability Insurance Predictive Modeling Applications July 30, 2009 Claim Analytics Inc. Jonathan Polon FSA

The Group DI Market DI vs WC Predictive Modeling for DI Questions Agenda

Group Disability Ins Market Annual premium of $13.5 Billion 75% Long Term Disability 25% Short Term Disability Market is very concentrated Top 5 players have 60% market share Top 10 players have 80% market share Source: JHA 2008 Group Disability Market Survey

DI vs Workers Compensation Both provide income replacement but DI coverage: is not mandated by law does not include medical expenses is typically not first day includes non-occupational causes of disability

DI vs Workers Compensation Modeling Considerations WC: Medical coverage  more information about each claim but also a larger tail DI: Non-occupational coverage  a greater range of diagnoses

Predictive Modeling Applications for Disability Insurance

1.Claim Approval SSA has developed the Quick Disability Determination program to identify claims in which: The claimant is most likely disabled There is evidence of the disability that can be easily and quickly obtained Pilot project: 3% of cases identified and 97% of these were decided within 21 days

2.Predicting Return to Work Predict the likelihood of return to work within various time horizons based on the specific attributes of a claim Improves claims management Improves disabled life reserving

Predicting Return to Work Claims Management Facilitates early intervention Improves allocation of resources Results in improved financial performance

Predicting Return to Work Disabled Life Reserves Reserves that are appropriate for each claim, rather than appropriate in aggregate Immediate reflection of change in claims mix Reduced volatility of financial results More accurate pricing and experience rating

3.Pricing / Ratemaking Disability insurers are lagging P&C insurers in adopting predictive modeling May be as few as 3 DI insurers using predictive modeling for pricing Accuracy is greatly improved compared to traditional methodologies

4.Benchmark Claim Experience Apply predictive modeling technologies to normalize for differences in business mix Create a standardized universe of claims For each company, predict outcomes for each claim in the standardized universe Predictions for each company are based on a predictive model, built from their own historic recovery experience

Benchmarking Results Companies are benchmarked on a comparable basis Performance can be measured as the expected recovery rate for each company Analysis provides insights into areas of strengths and weaknesses for each company

Benchmarking Performance Difference of 11% between best and worst performers

Questions