@ Hanover Insurance Group: Catherine Eska1

FROM CLASS TO INDIVIDUAL RATING CAS Predictive Modeling Seminar

October 4th, 5th 2006

Data Challenges and Considerations in Building a Modeling Dataset

@ Hanover Insurance Group: Catherine Eska2

Data Challenges and Considerations Main Topics

Data SourcesDealing with Product ChangesHow Detailed are your losses?Target Variable Considerations

@ Hanover Insurance Group: Catherine Eska3

Data Challenges and Considerations

Internal Data Sources:Considerations

How much history do you need? What is the most Complete and

Accurate Data Source? Where will the model obtain data once

you implement it? Need to capture and store model

results post implementation.

@ Hanover Insurance Group: Catherine Eska4

Data Challenges and Considerations

Internal Data Sources:Policy / Premium

Policy Processing System – Generally only has Written Premium– Most Complete data source– Where real-time scoring would happen– Available at the time the policy is quoted / issued

Statistical Record– Premium and Loss Data– Only updated at certain points in time (month-end)– Some codes converted from what is entered– Some data elements may be dropped– May include Manual Policy Data

@ Hanover Insurance Group: Catherine Eska5

Data Challenges and Considerations

Internal Data Sources:Claims / Losses

Statistical Record– Premium and Loss have same coding structure

(Major / Minor Line, Major Peril)– Assignment of Losses to Building / Location / Vehicle

may be suspect Claim System

– Codes most likely follow Policy Processing System– History may not be readily available– More accurate assignment of losses– Additional Data Elements may be available

@ Hanover Insurance Group: Catherine Eska6

Data Challenges and Considerations

Internal Data Sources:Balance and Verify

Most likely will get data from multiple sources

Make sure you balance the data: Premium $, Loss $, Counts (Policy and Claim)

Statistical Records usually most complete and accurate, so balance any data from other sources to this.

@ Hanover Insurance Group: Catherine Eska7

Data Challenges and Considerations

Internal Data Sources:Leave No Stone Unturned

Billing– May be a separate system– Some Billing attributes may be captured in the

Policy Processing System Agency Data

– Name and Address– Year Agent Appointed– Agent Status

Fraud / SIU Litigation

@ Hanover Insurance Group: Catherine Eska8

Data Challenges and Considerations External Data Sources

Vendors (Experian, ChoicePoint, D&B …) NOAA – Weather Data Census – Demographic Data WCRI / HLDI State Rating Bureaus NCCI / ISO Considerations: Cost / Appropriateness /

Regulatory

@ Hanover Insurance Group: Catherine Eska9

Data Challenges and Considerations

Product Changes:Coverage Examples

Rental Reimbursement Medical Payments Limit Embedded Limits:

– Jewelry, Watches and Furs– Accounts Receivable– Employee Dishonesty– Building and Ordinance

Broadening Endorsements

@ Hanover Insurance Group: Catherine Eska10

Data Challenges and Considerations

Product Changes:Coverage Treatment

Create an indicator to determine if the limit of coverage was selected by the insured or embedded in the base policy

Create a variable to represent what version of a broadening endorsement was on the policy

@ Hanover Insurance Group: Catherine Eska11

Data Challenges and Considerations

Product Changes:Other

Codes change – data dictionary – statistical manual

Definition changes – Age of Building versus Year Built

Indivisible Premium split to separate Coverage Premiums: Summarize at policy level (lowest common denominator)

@ Hanover Insurance Group: Catherine Eska12

Data Challenges and Considerations

How Detailed are your Losses?

It is generally desirable to build the model at the lowest level of detail that is accurate.– Personal Automobile – by Vehicle– Business Owner – by Building / Location– Workers Compensation – by State / Class

Heavily dependent on the quality of the individual company’s data.

When the detail is missing, you can get creative in your variable definitions

@ Hanover Insurance Group: Catherine Eska13

Data Challenges and Considerations

Losses should be accurate at the Policy

Level

You can not tie losses accurately to location / building / vehicle

Create Pseudo Variables:– Highest Building Value– Lowest Building Value– Number of Buildings– Deductible associated with Highest Building

Value

@ Hanover Insurance Group: Catherine Eska14

Data Challenges and Considerations

Alternatives to Policy Level:

State / Class Code Level Accuracy of loss assignment to state is

typically very good Accuracy of loss assignment to class

should be explored with Claims Very common for Commercial Policies

to have multiple states and classes on them

@ Hanover Insurance Group: Catherine Eska15

Data Challenges and Considerations

Coverage Treatment in Modeling

Pure Premium models are generally built by coverage.

Loss Ratio models are restricted to the level at which premiums are calculated

Can create variables that are specific to coverage within the modeling dataset: Liability Limit, General Liability Class Code, Industry (SIC or NAICS code), Dogs (Y/N), Toys (Jet Ski) etc …

@ Hanover Insurance Group: Catherine Eska16

Data Challenges and Considerations

Target Variable Considerations:

PRODUCT CHANGES

Adjust premiums to the new rate structure– Re-Rate historical policies using current rates– On-Level Factors

If you adjust the historical premiums, you must also adjust historical losses

Trend Losses in the History to reflect broader coverage Levels as well as inflation

Cap Losses in the Past to reflect more restrictive coverage levels (caps on replacement cost)

@ Hanover Insurance Group: Catherine Eska17

Data Challenges and Considerations

Target Variable Considerations:

TREND

When do you Adjust for Trend?

Loss Ratio Approach Using Premium at the current rate level

Select appropriate trend for losses by coverage

When don’t you Adjust for Trend?

Frequency/Severity Approach

Include Policy Year as a Dependent Variable

Is the trend implied by model reasonable?

@ Hanover Insurance Group: Catherine Eska18

Data Challenges and Considerations

Target Variable Considerations:

Loss Development

Claim data should be of sufficient maturity: Can you Limit dataset to Closed Claims? Chose an Age where “Pure” IBNR Claims are

no longer expected Desire that future development on known

claims is Minimal Balance between responsiveness and stability

@ Hanover Insurance Group: Catherine Eska19

Data Challenges and Considerations

Target Variable Considerations:

Loss Development

Build a GLM to model Loss Development Consider using Duration of the Claim

as a Dependent Variable Extrapolate duration of open claims

using a survival model Investigate other data elements

available in the Claims System … Claimant Age, Gender, Litigation Status

@ Hanover Insurance Group: Catherine Eska20

Data Challenges and Considerations

Target Variable Considerations:

Loss Development Options

Broad Loss Development Assumptions Not great at predicting actual ultimate loss at

a policy level Try to build assumptions for homogeneous

groups of claims: Coverage, Program, Industry, State …

If claims emergence is not complete: Earned Premium * Expected Loss Ratio * % Unreported

If claims are fully reported: Loss Development Factor * Reported Loss

@ Hanover Insurance Group: Catherine Eska21

Data Challenges and Considerations

Target Variable Considerations:

Loss Development

Implicit approach – No adjustment Include Open/Closed Indicator in

Severity Model Include Policy Year in Model and

Observe Implied Policy Year Trend Project trend coefficient for newer years

in setting up final model for implementation

@ Hanover Insurance Group: Catherine Eska22

Data Challenges and Considerations

Target Variable Considerations:

TREND

@ Hanover Insurance Group: Catherine Eska23

Data Challenges and Considerations Contact Information

Catherine E. Eska, FCAS, MAAAVice President Underwriting Analytics – Corporate Actuarial

The Hanover Insurance Group

440 Lincoln Street, S457Worcester, MA 01653ceska@Hanover.com(508) 855-2493