decision analytic products in us life insurance underwriting · proprietary and confidential | ©...
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Proprietary and Confidential | © General Reinsurance Corporation
Decision Analytic Products in US
Life Insurance Underwriting
Thomas Ashley, MD, FACP
Vice President and Chief Medical Director
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• CRL SmartScore, ExamOne Risk IQ
• Mine historical customer results of medical exam,
blood, urine
Decision Analytics Products
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 2
Industry
Lab Vendors
• Synthesize results across many clinical literature studies
into unified mortality risk equation
BioSignia
• Generate risk prediction from consumer behavior data Deloitte
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• Dataset of all lab customers who applied for insurance in
past 15 years
• Many millions of records with height, weight, blood
pressure plus results of blood and urine tests
• Social Security Death Master File to infer mortality outcome
• Construct integrated mortality risk prediction model
SmartScore and Risk IQ Common Threads
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 3
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Risk IQ SmartScore
Method • Generalized linear model
• Created synthetic variables such
as ratio of test results
• Excluded special selective tests
• Univariate relationship for
each test
• Assigned relative risk along
each curve
• Adjusted for age/sex
• Summed variables
• Added score for special tests PSA,
NT-proBNP, HCV
Output • Integer score, 0-99
• Approximates %ile mortality risk
by age/sex
• Score normed to approximate
credits (better than standard) or
debits (substandard)
Explanation • Both vendors report subscores that estimate contribution of single tests
to total score
SmartScore and Risk IQ Differences
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 4
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• Applicants only, no knowledge of underwriting result,
medical history
• Model inaccurate to extent that lab data duplicates known
medical risk (unless use model as substitute for other
underwriting)
• SSDMF incomplete
• Thus, each algorithm would look different if derived from
issued cases, adjusted for underwriting risk class, claims
Lab Models Common Weaknesses
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 5
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• Mortality of unplaced cases is invisible
• Use SSDMF to infer deaths
• Comparison to in-force mortality experience
• Measure accuracy of SSDMF against Gen Re claims
SSDMF Accuracy
Byproduct of facultative unplaced analysis
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 6
In-force All deaths observed
Unplaced Incomplete reporting, but by how much?
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SSDMF Accuracy
Age at Death
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40%
45%
50%
55%
60%
65%
70%
75%
80%
85%
90%
0-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90+
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• Claim analysis allows us to adjust for undetected deaths in
Facultative unplaced analysis
• Unclaimed property application of SSDMF
• Annuity surveillance
Implications
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 8
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• Goal is to integrate typical preferred underwriting criteria
(ht, wt, bp, family history, cholesterol, MVR, occupation)
• Appended select lab tests (glucose, liver enzymes)
• Meta-analysis: digest clinical literature to derive
relationship between each parameter and mortality risk
• Synthesize results across many studies into unified
mortality risk equation
• Output normed to approximate mortality % 2001VBT
Biomedical
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 9
BioSignia
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• Deloitte Consulting
• Ignore conventional underwriting evidence
• Mine electronic databases of consumer history
‐ Credit card purchases
‐ Warranty registration
‐ Survey responses
• Relate this profile to risk of disease and mortality
• Hundreds of parameters available for inclusion in model
• Construct unique model for each client company
‐ Choice of parameters to include / exclude
‐ Tune to customers of each company
Consumer Behavior
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 10
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• Multiple criteria for preferred considered separately
distorts overall measure of risk
• Prediction from integrated model might outperform
conventional underwriting of each variable separately
‐ More efficient risk classification
‐ Less overlap among risk classes
‐ Recognition of interactions that represent different risk than
sum of the parts
Upside
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Biomedical
• Faster, cheaper, automated underwriting without need for
blood, urine, exam
Deloitte
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• Demonstrate that score
corresponds to mortality
experience
• Industry labs
‐ Published performance on own data
‐ Unpublished trials for individual customers
• BioSignia
‐ Obtained large experience study data with
underwriting evidence
Validation
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 12
Mo
rta
lity A
/E
SCORE
Industry Labs,
BioSignia
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• Demonstrate that score corresponds to risk class
assignment from existing underwriting process
• Replication of underwriting action immediate – no need for
experience to develop or retrospective study
Validation
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 13
Conventional UW Class
Mo
de
l U
W C
lass
1 2 3
1
2
3
Deloitte
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• Hypothesis
‐ Refine preferred / STD risk and reclassify more consistently
‐ Qualify more applicants or adjust prices for risk classes
• Demonstrate efficacy
‐ Direct company could implement it
‐ Reinsurer could reflect it in pricing
‐ Regulator / producer could accept it
• Lab vendors derived model from insurance applicants /
SSDMF
• How does it perform on underwritten population?
• Single company study lacks power to measure low
risk groups
Gen Re Lab Score Validation Project
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 14
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• Measure performance of SmartScore and RiskIQ on
issued policies
Laboratory Mortality Risk Score Validation
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Goal
• Risk score construction used applicants and SSDMF
• Performance on issued lives and observed deaths will differ
• Decision on effective use of a score needs
inforce experience
Rationale
• Assemble underwriting evidence
• Obtain RiskIQ and SmartScore
• Assemble mortality experience
• Compare mortality risk prediction to mortality experience
Process
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Study Population Statistics
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 16
Lives Claims Maximum Duration Average Duration
1,211,741 2,348 8 Year
1.9 Year
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65.5% 73.3%
78.1% 84.1%
95.2%
0%
20%
40%
60%
80%
100%
40-69 70-74 75-79 80-83 84-110
Diastolic Blood Pressure
Conventional Underwriting Criteria
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 17
64.3% 70.8%
83.0% 81.4%
101.6%
0%
20%
40%
60%
80%
100%
120%
80-110 111-118 119-123 124-132 133-198
Systolic Blood Pressure
69.5% 69.3% 74.3%
93.8%
118.9%
0%
20%
40%
60%
80%
100%
120%
140%
16-23 24-26 27-29 30-34 35-50
BMI
81.6%
69.6% 75.6% 73.8%
86.3%
0%
20%
40%
60%
80%
100%
53-169 170-192 193-210 211-237 238-622
Cholesterol
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81.6%
69.6% 75.6% 73.8%
86.3%
0%
20%
40%
60%
80%
100%
53-169 170-129 193-210 211-237 238-622
Cholesterol
Cholesterol
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 18
68.5% 72.4%
78.1% 77.3%
98.4%
0%
20%
40%
60%
80%
100%
1.0-2.8 2.9-3.5 3.6-4.1 4.2-5.2 5.2-12.0
Cholesterol Ratio
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0%
20%
40%
60%
80%
100%
120%
40-69 70-74 75-79 80-83 84-110
Diastolic Blood Pressure
20-40
41-59
60+
Age Bands
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 19
0%
20%
40%
60%
80%
100%
120%
140%
160%
80-110 111-118 119-123 124-132 133-198
Systolic Blood Pressure
0-40
41-59
60+
0%
20%
40%
60%
80%
100%
120%
140%
160%
16-23 24-26 27-29 30-34 35-50
BMI
0-40
41-59
60+
0%
20%
40%
60%
80%
100%
120%
1.0-2.8 2.9-3.5 3.6-4.1 4.2-5.2 5.3-12.0
Cholesterol Ratio
0-40
41-59
60+
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Cholesterol Ratio Charts
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 20
68.5% 72.4%
78.1% 77.3%
98.4%
0%
20%
40%
60%
80%
100%
1.0-2.8 2.9-3.5 3.6-4.1 4.2-5.2 5.2-12.0
Cholesterol Ratio Group
0%
20%
40%
60%
80%
100%
120%
1.0-2.8 2.9-3.5 3.6-4.1 4.2-5.2 5.3-12.0
Cholesterol Ratio Group
0-40
41-59
60+
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49%
64%
73%
87%
126%
57% 59%
71%
81%
115%
0%
20%
40%
60%
80%
100%
120%
140%
160%
1 2 3 4 5
ExamOne
CRL
Lab Score Mortality Correlation
21 ACSW Dallas, TX | Thomas Ashley | November 7, 2014
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Comparison – Gender
22 ACSW Dallas, TX | Thomas Ashley | November 7, 2014
0%
20%
40%
60%
80%
100%
120%
140%
160%
1 2 3 4 5
ExamOne
Female
Male
0%
20%
40%
60%
80%
100%
120%
140%
160%
1 2 3 4 5
CRL
Female
Male
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Comparison – Age
23 ACSW Dallas, TX | Thomas Ashley | November 7, 2014
0%
20%
40%
60%
80%
100%
120%
140%
160%
1 2 3 4 5
ExamOne
0-46
47-57
58-99
0%
20%
40%
60%
80%
100%
120%
140%
160%
1 2 3 4 5
CRL
0-46
47-57
58-99
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• Multivariate score superior to single criteria
• Risk IQ and SmartScore identify highest risk
• High mortality rate sufficient to see at individual company
level
• Performance in low risk segments less striking but
meaningful
• Additional analysis–for participants only
- Greater detail, especially on low risk
- Additional stratification by gender, tobacco, duration,
underwriting risk class
Discussion
ACSW Dallas, TX | Thomas Ashley | November 7, 2014 24