using expert claims systems and reserving issues

CAS Spring Meeting

San Diego, CA

May 21, 2002

Using Expert Claims Systems and Reserving Issues

Considerations for The Potential Use of a Model

• Potential Uses of a Predictive Model Reserving Tool Settlement Tool Triage Tool


• Pros Greater Consistency Quicker Responsiveness to Inflation and Claim

Characteristic Shifts Better Ability to Triage Resources & Priorities Improved Allocation of Loss Makes Resources Available for Other Tasks Potential for Better Data Quality


• Cons Impact on Actuarial Methods Potential Loss of Expertise Cost to Implement and Maintain Creates Higher Budget for Settlement ???

Considerations WhileBuilding a Model

• Build Internally or License From Vendor Issues: Expertise, Cost, Black Box,

Maintenance

• Determination of Explanatory Variables Vendor’s Minimum Requirements Intuitive and Non-Intuitive Factors How Many Variables? Data Quality and Availability Issues


• Data Quality and Availability Issues Historical

Was the variable captured? How completely? How accurately?

Is the information readily available? Has the variable changed its meaning over time?


• Data Quality and Availability Issues Prospective

Will the variable be populated when needed? Will the variable be updated in a timely manner

when it changes? Who will be responsible for entry and data quality

going forward? Is the variable dependant on a derivation or is it

input directly?

Effect on Actuarial Practices

• Initial Measurement of Impact Triangles of Paid-to-Incurred Ratios Can Be

Analyzed to Gauge The Impact For a Relatively Stable Book of Business, The

Paid-to-Incurred Ratios Emerge in a Consistent Pattern

Deviation of “Post Model” Pattern From Historical Pattern Provides a Rough Estimate of the Impact


• Initial Measurement of Impact Need to Account for the Pace of

Implementation Useful to Perform Initial Measurement For

Different States, Loss Limits and Valuation Dates When Statistically Possible

Results From Initial Measurement Can Indicate Areas of Change That May Impact Actuarial Practices


Paid to Incurred Ratio-----------------Valuation------------------

Accident Year 12 15 18 21

1997 .435 .489 .600 .647

1998 .426 .491 .597 .642

1999 .466 .520 .611 .662

2000 .499 .556 .623 .644

Difference in Paid to Incurred Ratio Versus Accident Year 2000-----------------Valuation------------------

Accident Year 12 15 18 21

1997 .064 .067 .023 (.003)

1998 .073 .065 .026 .002

1999 .033 .036 .012 (.018)


• Issues for Loss Projections Ultimate Loss Projections

Limitations on Incurred Loss Methods During Transition

Paid Loss Methods Berquist/Sherman Method Use Findings from Initial Measurement of Impact Patterns Eventually Settle Into New Incurred Pattern Incurred Losses Usually More Volatile Anyway


• Issues for Loss Projections Other Areas Impacted

Internal Evaluations of Market Segments Potentially Better Replacing General IBNR With Specific Exposure Related

Case Reserve Critical for Actuaries to Make Sure Pieces Balance

Situations Where Raw Incurred Losses Are Used Accident Year Calendar Year


• Importance of Monitoring Usage Identification of Areas Where the Model Has

Issues Identification of Emerging Trends Effect on Loss Statistics Identification of Reserve Process Issues Preparation for the Next Update

Evaluating and UpdatingThe Model

• Accuracy Testing Runoff Studies

Compare Performance Versus Control Groups Posted Results of Segment Using Model Versus Segment

Not Using Model» Same Time Frame, Different Populations» Different Time Frame, Same Populations

Model Versus Posted Results Model Versus Expectations


• Accuracy Testing Runoff Studies (cont.)

Levels to Measure Relative Accuracy Aggregate Case Level

Critical to Consider Potential Biases Mix Issues Open Claims


• Important to Keep The Model Updated Claim Environment is Dynamic

Best Practices Changes Societal Changes Technology Changes Regulatory/Statute Changes

Data Underlying The Model Can Become Obsolete Quickly

Data Quality is Likely Improving


• Important to Keep The Model Updated Frequent Updates Soften The Magnitude of

Version Changes Frequent Updates Keep The Model Responsive

as Possible Tradeoff of Time and Resources


• Considerations in Model Updating Document Material Changes

Best Practices Acquisitions Book of Business Shifts

Gather The Opinions of the Model Users Review The Inventory of Model Variables

Possible Additions? Possible Removals? Data Quality Changed? Marginal Explanatory Value?


• Considerations in Model Updating Test Preliminary Model Update

Does It Address Known Issues? Does Anything Appear Counter-Intuitive?

Any Changes in the Way The Model is Used? Communication is Important

using expert claims systems and reserving issues

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