1. Integrating the Broad Range Applications of Predictive Modeling in a Competitive Market Environment Jun Yan Mo Mosud Cheng-sheng Peter Wu 2008 CAS Spring Meeting

2. - 2 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. Predictive modeling for pricing Predictive modeling for underwriting Predictive modeling for marketing Predictive modeling for pricing: most popular in actuarial Predictive modeling for underwriting: common in commercial lines Predictive modeling for marketing and sales: classic application for predictive modeling Three Major Types of Predictive Modeling in P&C Industry

3. - 3 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. Predictive Modeling for Pricing  Built on coverage/exposure level  Rating structure design  Determining loss cost relativities by rating factors  Typical approach: frequency/severity vs. pure premium

4. - 4 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. –Missing information –Miscoding –Losses below deductible not recorded –Losses above liability limit truncated –Data for modeling severity could be very thin –Inconsistency in exposure base from one coverage to another, from one class to another –For commercial lines, information kept at bureau class code reporting level, not at exposure level –CAT loss adjustment –Sparse data available for special lines and coverages –Adjustment for complex rating factors: Territory for personal lines Vehicle Symbol for personal auto Class code for commercial lines –Regulatory constraints Use of credit information Restrictions for variable selection, could be different by state Data Issues and Challenges for Pricing

5. - 5 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. Predictive Modeling for Underwriting  Evaluation of risk quality related to rating plan. Differentiate profitability by policy segments.  Assisting underwriters or product managers in underwriting: –Acceptance or rejection –Renewal or cancellation –Tier or company placement –Credit or Debit –Coverage limitations –Payment plan selection –Manual touch or automatic underwriting  Underwriting model design: –Policy level –Loss ratio as the target variable, frequency/severity approach is not commonly used –A wider selection of predictive variables Rating vs. non-rating variables Internal vs. external variables.

6. - 6 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. Data Issue and Challenges for Underwriting  Most of the data issues for pricing equally applicable to underwriting  Availability for non-rating variables, for example, billing data.  Actuarial adjustments for target loss ratio variable necessary: –Premium on-leveling –Loss development and trend  “Policy level” variables rolled up from the coverage and exposure level  Policy level underwriting models vs. account level underwriting models  Implementation consideration –Technology related –Regulation related –Business concerns

7. - 7 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. Predictive Modeling for Marketing and Sales Classic application of data mining and predictive modeling For insurance, there are four types of models: –New business qualification or targeting model: for example, mail solicitation for pre- qualified customers –New business conversion model: conversion rate from quote to binding –Renew business retention model: probability of an existing policy staying from current term to next term –Renew business conversion model: probability of conversion of a renewal policy to the next term at underwriting cycle.  Binary target for modeling: “success or failure”  A piece of “critical information” for marketing and sales models, “Price Elasticity”: –Premium comparison with major competitors, premium change at renewal –Other variables may affect price elasticity, including brand name, account indicator, policy age, etc

8. - 8 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. Predictive Modeling for Marketing A

9. - 9 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. Data Issues and Challenges for Marketing  For new business applications: –Quote files are not well stored, and the information on quote files are sparse: Name and address of an insured Basic and key rating information Agent information Competitiveness information including prior carrier’s name and price –For new business marketing models, need to rely on external databases: data quality and avaiability  For renewal business applications, lack of information for cancelled policies and competitors’ pricing data

10. - 10 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. Premium Optimization by Integrating Three Applications Premium optimization for P&C insurance is an approach to achieve an optimal outcome for an insurance company by balancing profitability and growth objective Premium optimization is built on top of the 3 major types of predictive modeling:  Underwriting Gain = Premium – Loss – Expenses  Expected Loss = Overall expected loss cost * rating plan factors * LR Relativity  Customer Marketing, Conversion, and Retention: New business marketing: campaign, solicitation and targeting New business conversion: price elasticity Renewal business retention: price elasticity Predictive modeling will be subjective to internal and external constraints.

11. - 11 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. Pricing Optimization Approach: Renewal Business

12. - 12 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. Integrating 3 Types Predictive Models  General Approach for Integration: Develop an “adequate” rating plan using the standard GLM approach:  The GLM rating plan would assume that the rate is adequate with regards to the rating variables and the structure of the rating plan Develop a new business conversion model or renew business retention model by studying the sensitivity of how insurance buyers react to price difference, such as the price elasticity Adjust the GLM rating plan so that the parameters can be re-optimized based on the conversion or retention model outcomes  Potentially many iteration and time consuming. Build underwriting models on top of the pricing and marketing models.

13. - 13 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. Advantages for the Integration  Underwriting and marketing models are flexible in dealing with the dynamic external environment  The subjective judgment by underwriters can be largely eliminated  Optimize between premium growth and profitability  “Fine tune” the pricing strategy: –For example, adjust the rates for the most price sensitive segments, instead of taking uniform, comprehensive rate adjustments

14. - 14 - Copyright © 2008 Deloitte Development LLC. All rights reserved. Confidential and Proprietary - Do not Copy to Distribute. Data Issues and Challenges for the Integration  The data level is different between the 3 types of models  Marketing applications are “forward-looking” based, while the pricing and underwriting applications are based on “historical” information  Change in distribution for book mix  Change in distribution for premium size  Change in distribution channels or affinity programs  Data is more sparsely available for the marketing application than for the underwriting or pricing applications.