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Transcript of Discovery Through Statistics Claim Analytics Group Disability Reserving Unleash the Power of the 21...
Discovery Through Statistics
Claim Analytics
Group Disability ReservingGroup Disability ReservingUnleash the Power of the 21Unleash the Power of the 21stst Century Century
Canadian Canadian Institute of ActuariesInstitute of ActuariesJune 29 2006June 29 2006
Barry Senensky FCIABarry Senensky FCIA
www.claimanalytics.comwww.claimanalytics.com
Discovery Through Statistics
Claim Analytics
• Evolution of reserve calculations
• Where are we today?
• How can we improve the process?
• Summary
AgendaAgenda
Discovery Through Statistics
Claim Analytics
Evolution of Evolution of Reserve Reserve
CalculationsCalculations
Discovery Through Statistics
Claim Analytics
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
Discovery Through Statistics
Claim Analytics
1960’s 1960’s
Discovery Through Statistics
Claim Analytics
1960’s 1960’s Mainframe/manual Simplified formulas Conservative
assumptions Infrequent
experience and table updates
Technology• Paper• Very early computers• Tapes, disks
Evolution of Reserve CalculationsEvolution of Reserve Calculations
Discovery Through Statistics
Claim Analytics
1970’s 1970’s Evolution of Reserve CalculationsEvolution of Reserve Calculations
Discovery Through Statistics
Claim Analytics
1980’s 1980’s Evolution of Reserve CalculationsEvolution of Reserve Calculations
Discovery Through Statistics
Claim Analytics
Evolution of Reserve CalculationsEvolution of Reserve Calculations
Mainframe calculations Move to basic principles
calculations Conservative assumptions Infrequent table updates
and experience studies
Technologyo Improved
mainframeso PCs
1970’s - 1980’s1970’s - 1980’s
Discovery Through Statistics
Claim Analytics
1990’s 1990’s
Evolution of Reserve CalculationsEvolution of Reserve Calculations
Tim Berners-Lee
Ignites the Internet
Discovery Through Statistics
Claim Analytics
1990’s 1990’s
Mainframe/PC calculations Basic principles calculations Expected assumptions with
explicit margins Deterministic scenario
testing Infrequent table updates Annual / bi-annual
experience studies
Technology• Faster computers,
more storage• Online processing• Internet
Evolution of Reserve CalculationsEvolution of Reserve Calculations
Discovery Through Statistics
Claim Analytics
Today Today PC-based calculations Basic principles Stochastic modeling Expected assumptions
with explicit margins Infrequent table
updates Annual/bi-annual
experience studies
Technology• Advanced software
algorithms• Powerful computers with
more storage, faster processing,
• Access to large databases of historic information
Evolution of Reserve CalculationsEvolution of Reserve Calculations
Discovery Through Statistics
Claim Analytics
What Have We Accomplished?What Have We Accomplished?
Tremendous progress made possible by evolution of computer power
Calculations now explicit and seriatim Scenarios sensitivity-tested to better
evaluate risk Experience studies more frequent
Discovery Through Statistics
Claim Analytics
What do we still need to do?What do we still need to do?Group Disability ReservingGroup Disability Reserving
Frequent and comprehensive experience informationo Studies at least annuallyo Ability for user to slice-and-dice information
Information electronically provided to users
Why?o More appropriate, up-to-date experience information
Obstacleso Lack of priority
Discovery Through Statistics
Claim Analytics
Need key predictive factors of recovery, particularly diagnosis, but also Quebec, monthly benefit, tax status, reporting lag, incorporated into reserve calculation
Why?
o More appropriate reserve for each claim o Immediately capture business mix changeso Eliminate cherry picking at quarter-endso Align with claim management practiceso More understandable to management o Relevant for experience rating situations
Obstacleso Lack of training in predictive modeling o How do you do it? (There can be thousands of diagnoses)o Cost to implement
What do we still need to do?What do we still need to do?Group Disability ReservingGroup Disability Reserving
Discovery Through Statistics
Claim Analytics
How do we build diagnosis into How do we build diagnosis into reserve calculations?reserve calculations?
Discovery Through Statistics
Claim Analytics
o Classify each diagnosis into a set of categories, based on likelihood and time to recovery
o Develop unique termination rates for each category
Weaknesses: • Labour-intensive• Subjective• Data credibility issues
Method 1
Building diagnosis into reserve calculationsBuilding diagnosis into reserve calculations
Discovery Through Statistics
Claim Analytics
Building diagnosis into reserve calculationsBuilding diagnosis into reserve calculations
o Use predictive modeling techniques to produce scores that equate to probabilities of recovery or termination
o Calculate reserves directly, using scores
SOA paper outlines methodology for creating scores
Scores are proven and credible
Method 2
Discovery Through Statistics
Claim Analytics
Using Predictive Modeling Using Predictive Modeling to Calculate Reservesto Calculate Reserves
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
…
…
…
…
…
Claim # Elim Diagnosis Sex Age Benefit (Other ) 6M 24M
451122 119 Depression Reactive (Prolonged)
M 42 1411 7 10
452024 364 Tear Medial Meniscus (Knee)
M 47 2500 4 7
452141 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 24 months, then use traditional methods thereafter
• Produce scores for all future terms
Discovery Through Statistics
Claim Analytics
Significant progress has been made in calculating reserves.
Still needed in Group Disability reserving:
• Better experience information
• Reserves that explicitly reflect the key factors for termination
This is all doable today.
Summary