Challenges with Incorporating Predictive Models within the Underwriting Process

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Challenges with Incorporating Predictive Models within the Underwriting Process

Presented by:

Daniel Roth, FCAS, MAAAVice President & Actuary/Pricing

Standard LinesCNA

Chicago, Illinois

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Challenges with Incorporating Predictive Models within the Underwriting Process

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.

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Challenges with Incorporating Predictive Models within the Underwriting Process

Detailed Description of the Model

Can’t supply because:

1.Confidentiality reasons; it is propriety to company

2.Too theoretically complex

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Challenges with Incorporating Predictive Models within the Underwriting Process

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

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Challenges with Incorporating Predictive Models within the Underwriting Process

In Simple Language

It just blends the underwriting thought process together into one ranking for the

risk in an objective and consistent approach.

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Challenges with Incorporating Predictive Models within the Underwriting Process

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

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Challenges with Incorporating Predictive Models within the Underwriting Process

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.

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Challenges with Incorporating Predictive Models within the Underwriting Process

Where is the Predictive Model Currently Used?

Small Business Accounts

LOB New BusinessRenewals

BOPs 2003 2001Auto 2003 2002Packages 2004 2001WC 2003 2002

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Challenges with Incorporating Predictive Models within the Underwriting Process

Expected Results

Objectives• Enhances risk selection and pricing

Benefits• Loss Ratio Improvement• Operational Efficiencies• Better Retentions• Appropriate Pricing• Supports Company and State Compliance

Requirements

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Challenges with Incorporating Predictive Models within the Underwriting Process

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 strategies2.CAT strategies3.Class strategies

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Challenges with Incorporating Predictive Models within the Underwriting Process

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

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Challenges with Incorporating Predictive Models within the Underwriting Process

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

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Challenges with Incorporating Predictive Models within the Underwriting Process

Workers’ Compensation Results (Small Business)

Above BelowSuperior Average Average

AverageRetention

2004 92.0% 90.5% 85.0% 48.6%2005 91.3 91.5 90.1 58.3

Rate Change2004 -0.5% 0.6% 0.8% 2.3%2005 -0.3 0.6 1.0 2.3

Relative Claim Frequency Per $1,000 Premium2004 0.66 0.93 1.63 3.022005 0.60 0.99 1.59 1.59

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Challenges with Incorporating Predictive Models within the Underwriting Process

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?

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Challenges with Incorporating Predictive Models within the Underwriting Process

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

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Challenges with Incorporating Predictive Models within the Underwriting Process

Middle Markets

• Looking to expand concepts into Middle Markets

• For Small Business1.Majority of accounts are renewed per guideline

instructions via the policy issuance processors2.Majority of new accounts are issued via agents per

their authority3.Underwriters only see exceptions

• For Middle Markets, the focus is more on consolidation of documentation and file documentation of the underwriting thought process

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Challenges with Incorporating Predictive Models within the Underwriting Process

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.