discovery through statistics claim analytics

Discovery Through Statistics

Claim Analytics

Renewing LTDRenewing LTD Using Data Mining TechniquesUsing Data Mining Techniques

Canadian Canadian Institute of ActuariesInstitute of ActuariesNovember 10, 2005November 10, 2005

Barry Senensky FCIABarry Senensky FCIA

www.claimanalytics.comwww.claimanalytics.com


Claim Analytics

• Data mining

• Claims scoring

• Using claim scoring to develop LTD reserve termination assumptions

AgendaAgenda


Claim Analytics

Data MiningData MiningDefinedDefined

• Extraction of previously unknown information from large data sets or databases

• Finding and quantifying of hidden patterns and trends in databases


Claim Analytics

Data Mining Data Mining ApplicationsApplications

• Used extensively in industry:• Credit card and tax fraud detection

• Credit scoring

• Weather prediction

• Handwriting to text conversion

• Many, many other applications


Claim Analytics

Data Mining ToolsData Mining Tools

1. CART

2. Neural Networks

3. Genetic Algorithms

Filter.

Optimization tools

Identifies factors withgreatest impact.


Claim Analytics

Neural Networks / Genetic AlgorithmsNeural Networks / Genetic Algorithms

How they learnHow they learn

Model is presented with data sample with known outcomes

Model predicts result, then compares it to actual outcome

Model parameters are changed to better approximate the sample…

…Over and over again.


Claim Analytics

Claims are scored from 1 to 10.

Scores show likelihood of return to work within a given timeframe.

Scores are calibrated: • score of 1 indicates 0 – 10%

chance of recovery within given timeframe, score of 2 indicates 10 – 20% chance of recovery within given timeframe, and so on.

J. Spratt Score: 4# 452135

ClaimsClaims ScoringScoring

J. Loe Score: 6# 452009

P. Chang Score: 8# 451156


Claim Analytics

…

…

…

…

…

Claim # Elim Diagnosis Sex Age Benefit (Other ) 6M 24M

451156 119 Depression Reactive (Prolonged)

M 42 1411 7 10

452009 364 Tear Medial Meniscus (Knee)

M 47 2500 4 7

452135 180 Fibromyalgia F 37 3899 6 6

452338 180 Major Depressive Disorder

F 35 1773 6 8

452341 119 Lumbar Disc Degen/Disease

M 42 1150 2 5

452494 210 Herniated Disc Acute F 59 3564.9 2 2

ScoringScoring ReportReport

Q.P.


Claim Analytics

Five steps to developing Five steps to developing LTD termination rates for DaveLTD termination rates for Dave

using claim scoringusing claim scoring

Dave


Claim Analytics

About Dave

Sex Male

Age 44

QP 90 days

Diagnosis Osteoarthritis

Developing termination Developing termination rates for Daverates for Dave


Claim Analytics

Dave’s claim scores

Likelihood of RTW (%)

3 months 5.96 months 14.712 months 27.524 months 34.5



Claim Analytics

•cumulative RTW Probabilities, 1-24 Months after EP

•expressed as %

1 2 3 4 5 6 7 8 9 10 11 12

5.9 14.7 27.5

13 14 15 16 17 18 19 20 21 22 23 24

34.5

Step One

Get Cumulative RTW Probabilities



Claim Analytics

1 2 3 4 5 6 7 8 9 10 11 12

2.0 3.9 5.9 8.8 11.8 14.7 16.8 19.0 21.1 23.2 25.4 27.5

13 14 15 16 17 18 19 20 21 22 23 24

28.1 28.7 29.3 29.8 30.4 31.0 31.6 32.2 32.8 33.3 33.9 34.5

• choose uniform distribution, constant force or Balducci

• here, used uniform distribution

• expressed as %

Developing termination Developing termination rates for Daverates for DaveStep Two

Interpolate between months


Claim Analytics

• Canadian Group LTD experience /1000 shown here

• alternative is company experience

• may want to make adjustments, e.g. improvement from mid-point of study

1 2 3 4 5 6 7 8 9 10 11 12

.27 .32 .40 .45 .49 .51 .52 .53 .52 .52 .50 .49

13 14 15 16 17 18 19 20 21 22 23 24

.47 .46 .44 .42 .40 .38 .37 .35 .34 .32 .31 .29

Step Three

Get mortality rates



Claim Analytics

1 2 3 4 5 6 7 8 9 10 11 12

1.97 2.00 1.99 2.96 2.98 2.97 2.15 2.12 2.11 2.10 2.10 2.09

13 14 15 16 17 18 19 20 21 22 23 24

.57 .56 .56 .56 .55 .55 .55 .55 .55 .55 .55 .55

Step Four

Convert cumulative RTW probabilities to month-to-month RTW rates

# of claimants who will recover in

period.


1 - LM cumulative RTW - LM cumulative death rate

TM cumulative RTW - LM cumulative RTW

# of claimants still on claim at start of period.


Claim Analytics

1 2 3 4 5 6 7 8 9 10 11 12

2.24 2.32 2.39 3.41 3.47 3.48 2.67 2.65 2.64 2.62 2.60 2.58

13 14 15 16 17 18 19 20 21 22 23 24

1.04 1.02 1.00 .98 .96 .94 .92 .90 .88 .87 .85 .84

Step Five

Calculate Termination Rates

• Termination rate = recovery rate + mortality rate



Claim Analytics

What to do after 24 months

• Produce scores for 36 months, then use traditional methods thereafter

• Produce scores for all future terms


Claim Analytics

Credibility

• Significant benefits over traditional methods:

• Rates are based on internal experience

• Data mining offers advantages over table of claims analysis

Table of Claims Data Mining

Accuracy Accurate in aggregate Allows reserves to be accurately allocated between claims: important for renewal pricing, experience-rated refunds etc.

Sensitivity Sensitive to changes in the age / elimination period distribution of claims.

Sensitive to many other factors as well: diagnosis, gender, income, province, occupation, etc.


Claim Analytics

Credibility

Testing the model • Normally use back-testing to confirm fit of model


Claim Analytics

Back-testing the Scoring Model

Recovery by Score - Validation Sample0 - 24 Months

0%

20%

40%

60%

80%

100%

Rec

over

y R

ate

6

8

10

12

14

16

Recovery Rate 8% 20% 24% 37% 40% 54% 61% 79% 91% 96%

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

# of Claims 34 69 94 86 129 111 86 103 86 60

1 2 3 4 5 6 7 8 9 10


Claim Analytics

Benefits

• More appropriate reserve for each claim, avoid “averages of averages”

• Aligned with claim management practices

• Facilitates repricing / renewal

• Earlier recognition of changes in trends and experience


Claim Analytics

Claim scoring offers a new and innovative way of setting LTD termination rates that results in a more appropriate reserve for each claim.

Summary

discovery through statistics claim analytics

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