Barry Senensky FSA FCIA MAAA

www.claimanalytics.com

Overview of Claim ScoringOverview of Claim ScoringNovember 6, 2008November 6, 2008

On the AgendaOn the Agenda

About us

What is Claim Scoring?

Predictive Modeling

Building a Claim Scoring Model

Using Claim Scoring

Summary

Questions & Answers

• Founded in 2001 by two actuaries to apply predictive modeling techniques to insurance questions

• Clients in Canada and U.S.

• Several products

About UsAbout Us

What is claims scoring?

LTD/STD claims scored from 1 to 10, based on likelihood of recovery within a given timeframe

Scores are objective and accurate

Scores calibrated to probability of recovery

WhatWhat is claims scoring?is claims scoring?

J. Spratt Score: 4/6# 452135

P. Can Score: 3/9# 451156

J. Loe Score: 5/7# 452009

Predictive Modeling

Computer PerformanceComputer Performance

Measure IBM 7094

c. 1967

Laptop

c. 2004

Change

Processor Speed (MIPS)

.25 2,000 8,000-fold increase

Main Memory

144 KB 256,000 KB 1,778-fold increase

Approx. Cost ($2003)

$11,000,000 $2,000 5,500-fold decrease

What is a Predictive ModelWhat is a Predictive Model• A Predictive Model is a model which is

created or chosen to try to best predict the probability of an outcome

• Have been around for 40+ years

• Harnesses power of modern computers to find hidden patterns in data

• Used extensively in industry

• Many possible uses in insurance:

About Predictive ModelsAbout Predictive Models

May be parametric…

• apply numerical methods to optimize parameters

• E.g., gradient descent, competitive learning

Or non-parametric

• often have a decision tree form

• typically optimized using exhaustive search

Predictive Modeling ToolsPredictive Modeling Tools

Some common techniques

• Generalized linear models

• Neural networks

• Genetic algorithms

• Random forests

• Stochastic gradient boosted trees

• Support vector machines

Why aren’t Insurance Why aren’t Insurance Companies building more Companies building more predictive models?predictive models?• Life & Health Insurance Industry is conservative

and can be slow to change• Not a traditional actuarial tool• The times are changing!

– Especially P&C Insurers• Its only a matter of time!

– It just makes too much sense! – Innumerable applications to help solve

insurance problems

Building a Claim Scoring Model

Start with a data extract:

- Age - EP- Gender - Diagnosis- 2nd diagnosis - Income- Benefit - Occupation- Region - Own occ period- Industry - and more

Building the ModelBuilding the Model

Building the ModelBuilding the Model

1. Model presented with your historic claim data, including known outcomes.

2. Model begins making predictions on cases in the sample…

3. …compares predictions to real outcomes, and begins to detect patterns…

Initial predictions are rough…

But… model continues to learn

After millions of iterations and millions of comparisons… the model learns to predict accurately

And builds a complex algorithm that fits your data

Model ValidationModel Validation

• Critical test of model’s accuracy

• Outcomes of 10% of historical data withheld by client

• Once model declared complete, this data is used to test model — compare model predictions to actual outcomes

Model Validation ResultsModel Validation Results

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Reco

very

%

Pred Rec % 5% 15% 25% 35% 45% 55% 65% 75% 85% 95%

Recovery % 7% 12% 19% 32% 43% 57% 69% 76% 81% 92%

1 2 3 4 5 6 7 8 9 10

Model’s Predicted Recovery

Actual Recovery Rate

1. Scores can be calculated for all in-force claims

2. New claims can be scored weekly or even sooner

Claim Scoring ProcessClaim Scoring Process

Claim # Name EP Diagnosis Sex Age Benefit 6m Score

24m Score

12798 P.Can 119 Torn Medial Meniscus

M 52 1,250

12804 J.Loe 180 Fibromyalgia

F 46 2,500

12846 J.Spratt 364 Fibromyalgia

F 46 2,900

ReportingReporting

Note: actual reporting includes more fields than shown here.

Claim # Name EP Diagnosis Sex Age Benefit 6m Score

24m Score

12798 P.Can 119 Torn Medial Meniscus

M 52 1,250 3 9

12804 J.Loe 180 Fibromyalgia

F 46 2,500 5 7

12846 J.Spratt 364 Fibromyalgia

F 46 2,900 4 6

Using claim scoring

Are you kidding me?Are you kidding me?

&

Objective Triage

Facilitate early interventionFacilitate early intervention

Review of Old In-force Review of Old In-force ClaimsClaims

• High scores — opportunities for recovery

• Low scores — opportunities for expense savings

Discover new opportunities

Workload AllocationWorkload Allocation• Claims can be allocated by degree of challenge

• 4s to 7s more difficult, time-intensive — more experienced and expert claims handlers

• 1s to 3s, 8s to 10s simpler — newer / less experienced claims handlers

• Equalize workload of claims personnel

Smooth the workload

Prioritize TimePrioritize Time• Can be used by claims handlers to prioritize their

time

Snapshot of your workload

Social Security Social Security // Other Offsets Other Offsets

• 1s to 3s are good candidates to review

• Even better to build a model specific to determining which claims to send to Social security and when…

Learn when to reach out

Decision Support ToolDecision Support Tool

• Rehab

• IME’s

• Surveillance

• Other forms of intervention

• Settlements

Optimize resource $

MeasurMeasuree performance performance

• Scores represent expected recovery rates

• Can be used to measure actual to expected (A/E) recoveries

What you can measure, you can improve

Actual Recovery

%

Predicted Recovery

%

A / E

Regional Office 1

30 27 111

Regional Office 2

50 61 82

Planning/ForecastingPlanning/Forecasting

• Scores indicative of future recovery experience

• Use to develop financial projections for group business unit.

Don’t get blindsided!

Reporting: trend Reporting: trend identificationidentification

Average 24 Month Score

3.00

4.00

5.00

6.00

7.00

Score 5.78 5.68 5.63 5.55 5.47 5.37 5.34 5.23 5.15 5.07 5.02 4.94

May 03 Jun 03 Jul 03 Aug 03 Sep 03 Oct 03 Nov 03 Dec 03 Jan 04 Feb 04 Mar 04 Apr 04

BenchmarkingBenchmarking

• Accurately, objectively compare claim practices with other companies

How are we really doing?

Sample Predicted Recovery RateSample Predicted Recovery Rate

Predicted Recovery Rate:Age 45, Male, Displaced Disk

0%

20%

40%

60%

80%

100%

Predicted 81% 65% 74% 73%

Company A Company B Your Company Average

SummarySummary

Fast. Accurate. Objective.

Optimize resources.

Facilitate early action.

Improve results.

Opportunities in approach. Opportunities in approach.

AfterBefore

Questions?Questions?