Challenges with Incorporating Predictive Models within the Underwriting Process

2 Presented by: Daniel Roth, FCAS, MAAA Vice President & Actuary/Pricing Standard Lines CNA Chicago, Illinois

Challenges with Incorporating Predictive Models within the Underwriting Process 3 What is a Predictive Model? Uses multiple data variables on an individual risk to develop a ranking which identifies the relative likelihood of insurance loss Data variables can be traditional or non-traditional from both internal or external sources The ranking is a predictive measure of future profit potential based upon the risk characteristics only If the risks are grouped into 10 buckets the model should place approximately 10% of the risks in each bucket.

Challenges with Incorporating Predictive Models within the Underwriting Process 4 Detailed Description of the Model Can’t supply because: 1.Confidentiality reasons; it is propriety to company 2.Too theoretically complex

Challenges with Incorporating Predictive Models within the Underwriting Process 5 What a Predictive Model is/does NOT It is not a rating engine It is not an underwriting guideline It does not apply schedule rating or IRPMs It does not tier the business It does not accept, reject, or non renew policies It does not say whether one state is better than another It does not say whether one class is better than another

Challenges with Incorporating Predictive Models within the Underwriting Process 6 In Simple Language It just blends the underwriting thought process together into one ranking for the risk in an objective and consistent approach.

Challenges with Incorporating Predictive Models within the Underwriting Process 7 Testing the Model Before it went live: Determined the weighting of variables using a sampling of approximately 50,000 risks. Applied the model to another set of approximately 30,000 risks to produce lift curves Had a lot of meetings with the LOB Underwriting VPs to convince them of the models validity

Challenges with Incorporating Predictive Models within the Underwriting Process 8 Why do we use a Predictive Model? 1.We use a Predictive Model to improve and sustain our overall profitability by identifying business that presents lower underwriting risk 2.It is also one of the best ways to manage a large book of business where it is cost-prohibitive to conduct a traditional type of review on every account. 3.Use of this model also offers a consistent way to achieve the profitability on the book.

Challenges with Incorporating Predictive Models within the Underwriting Process 9 Where is the Predictive Model Currently Used? Small Business Accounts LOBNew BusinessRenewals BOPs Auto Packages WC

Challenges with Incorporating Predictive Models within the Underwriting Process 10 Expected Results Objectives Enhances risk selection and pricing Benefits Loss Ratio Improvement Operational Efficiencies Better Retentions Appropriate Pricing Supports Company and State Compliance Requirements

Challenges with Incorporating Predictive Models within the Underwriting Process 11 How to Use the Predictive Model Information Incorporate into mutually exclusive Underwriting Guidelines for risk selection, renewal activity, and pricing May need to supplement the model with: 1.State strategies 2.CAT strategies 3.Class strategies

Challenges with Incorporating Predictive Models within the Underwriting Process 12 Pricing Approaches Rate Expectations (Renewals) Disadvantage: Could neutralize filed class relativity changes in a given state Tier Movement (Renewals) Advantage: Minimizes rate swings and assumes original placement in the expiring tier was already reflected via the prior underwriting review Tier Placement (New Business and Renewals) Advantage: Point in time underwriting decision regardless of prior thought process supporting truly mutual exclusive placement

Challenges with Incorporating Predictive Models within the Underwriting Process 13 Supporting Compliance Must incorporate into any filed Underwriting guidelines or Predictive Model cannot be utilized File documentation Objective and consistent underwriting approach for ‘like’ risks

Challenges with Incorporating Predictive Models within the Underwriting Process 14 Workers’ Compensation Results (Small Business) AboveBelow SuperiorAverageAverageAverage Retention %90.5%85.0%48.6% Rate Change %0.6%0.8%2.3% Relative Claim Frequency Per $1,000 Premium

Challenges with Incorporating Predictive Models within the Underwriting Process 15 But Does It Really Work? Then how come we have seen similar patterns in lines when the Predictive Model was not yet ‘fully’ implemented?? A good group of underwriting and policy issuance processors Objective file documentation of underwriting thought process Objective calculation of Non Rate Underwriting Impact for loss ratio projections But, should the slopes be steeper?

Challenges with Incorporating Predictive Models within the Underwriting Process 16 Model Upkeep 1.Adjust objective pricing direction ongoing, if necessary, based upon recent rate filings 2.Refresh data for necessary variables at least biennially 3.Recalibrate data between the variables periodically

Challenges with Incorporating Predictive Models within the Underwriting Process 17 Middle Markets Looking to expand concepts into Middle Markets For Small Business 1.Majority of accounts are renewed per guideline instructions via the policy issuance processors 2.Majority of new accounts are issued via agents per their authority 3.Underwriters only see exceptions For Middle Markets, the focus is more on consolidation of documentation and file documentation of the underwriting thought process

Challenges with Incorporating Predictive Models within the Underwriting Process 18 Disclaimer The purpose of this presentation is to provide general information about CNA and its current predictive modeling strategies. Given the strategies’ unique fit with CNA, they may or may not be appropriate for use by other companies. CNA is a service mark registered with the U.S. Patent and Trademark Office. Copyright © 2006, Continental Casualty Company. All rights reserved.