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Challenges with
Incorporating Predictive
Models within the
Underwriting Process
Challenges with Incorporating Predictive Models within the
Underwriting Process
Presented by:
Daniel Roth, FCAS, MAAA
Vice President & Actuary/Pricing
Standard Lines
CNA
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 costprohibitive 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 Business
Renewals
BOPs
Auto
Packages
WC
2003
2003
2001
2002
2004
2001
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 strategies
2. CAT strategies
3. 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
Superior Average Average Average
Below
Retention
2004
2005
92.0%
91.3
90.5%
91.5
85.0%
90.1
48.6%
58.3
Rate Change
2004
2005
-0.5%
-0.3
0.6%
0.6
0.8%
1.0
2.3%
2.3
Relative Claim Frequency Per $1,000 Premium
2004
0.66
0.93
2005
0.60
0.99
1.63
1.59
3.02
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 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
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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.
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