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
Discovery Through Statistics
Claim Analytics
• Data mining
• Claims scoring
• Using claim scoring to develop LTD reserve termination assumptions
AgendaAgenda
Discovery Through Statistics
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
Discovery Through Statistics
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
Discovery Through Statistics
Claim Analytics
Data Mining ToolsData Mining Tools
1. CART
2. Neural Networks
3. Genetic Algorithms
Filter.
Optimization tools
Identifies factors withgreatest impact.
Discovery Through Statistics
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.
Discovery Through Statistics
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
Discovery Through Statistics
Claim Analytics
…
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…
…
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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.
Discovery Through Statistics
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
Discovery Through Statistics
Claim Analytics
About Dave
Sex Male
Age 44
QP 90 days
Diagnosis Osteoarthritis
Developing termination Developing termination rates for Daverates for Dave
Discovery Through Statistics
Claim Analytics
Dave’s claim scores
Likelihood of RTW (%)
3 months 5.96 months 14.712 months 27.524 months 34.5
Developing termination Developing termination rates for Daverates for Dave
Discovery Through Statistics
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
Developing termination Developing termination rates for Daverates for Dave
Discovery Through Statistics
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
Discovery Through Statistics
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
Developing termination Developing termination rates for Daverates for Dave
Discovery Through Statistics
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.
Developing termination Developing termination rates for Daverates for Dave
1 - LM cumulative RTW - LM cumulative death rate
TM cumulative RTW - LM cumulative RTW
# of claimants still on claim at start of period.
Discovery Through Statistics
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
Developing termination Developing termination rates for Daverates for Dave
Discovery Through Statistics
Claim Analytics
What to do after 24 months
• Produce scores for 36 months, then use traditional methods thereafter
• Produce scores for all future terms
Discovery Through Statistics
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.
Discovery Through Statistics
Claim Analytics
Credibility
Testing the model • Normally use back-testing to confirm fit of model
Discovery Through Statistics
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
Discovery Through Statistics
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
Discovery Through Statistics
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