Guizhou Hu, MD, PhD

Guizhou Hu, MD, PhDExecutive Vice President & Chief Scientific Officer BioSignia, Inc.

© BioSignia, Inc. 2012 CONFIDENTIAL DO NOT COPY OR DISTRIBUTE WITHOUT PERMISSION OF BIOSIGNIA INC.

Agenda

• What is Predictive Analytics?

• Predictive Analytics in life insurance

• An example of predictive model in life insurance underwriting

• Epidemiological research: the foundation of predictive models for life insurance underwriting

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Predictive Analytics: What is it?

According to the Society of Actuaries, predictive modeling is:

“A process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with

forecasting probabilities and trends. A predictive model is made up of a number of predictors, variable factors that are likely to influence or predict future

behavior. The end result is both a set of factors that predict, to a relatively high degree, the outcome of an event, as well as what that outcome will be. In

marketing, for example, a customer’s gender, age and purchase history might predict the likelihood of a future sale. To create a predictive model, data is

collected for the relevant factors, a statistical model is formulated, predictions are made and the model is validated. The model may employ a simple linear

equation or can be a complex neural network or genetic algorithm.”

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Examples of Predictive Models

• LinkedIn connection recommendations• NCAA Tournament bracket tools• Amazon.com product recommendations• Framingham cardiovascular model

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Attributes of Good Predictive Models

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• How was the model built?– Empirically(objective)-based or non-empirically

(subjective) based– Based on single dataset or multiple data.

• How was the model validated?– External and internal validation– Two statistical features measure prediction model

performance:

• Calibration: How close the prediction matches the observed outcomes among different risk groups

• Discrimination: How much the model differentiates risk between individuals who die and those who live


Predictive Analytics in Life Insurance

• Target marketing• Fraud detection • Product design• Risk selection / underwriting

– Examples of predictive tools in underwriting:• Debit/credit scoring• Rules-engines• Financial credit scores (Deltoid)• MAT (BioSignia)• Lab scores (Exame One, CRL..)

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Industry Forces Shaping Adoption of Predictive Analytics

Industry Drivers

Data availability

Technological advances

Pressure for growth in slow markets

Availability of analytic talent

Pressure for competitive advantage

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Industry Reservations

“black box” perception

Limited validation

Competing internal priorities

Lack of IT resources

Regulatory concerns


Example of Predictive Analytics in life Insurance Underwriting

Mortality Assessment Technology (MAT)• How MAT was developed?

– Techniques

• Meta Analysis: summary of medical literature• Synthesis Analysis: integrate the multiple risk

factor into multivariate prediction equation.

– Contents: Primarily based on epidemiological research in medical fields

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How MAT Was Validated

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• Data set– ~311,000 historically issued policies– Up to 9 years death claims– Apply MAT along with conventional underwriting guidelines to the

underwriting data• Specific questions:

– Does the predicted mortality from MAT closely match the observed mortality? (calibration)

– Does the risk class defined by MAT have greater mortality differentiation than conventional underwriting? (discrimination)


Validation Results

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Conventional Classes

Class % PAE A/E

1 39% 0.5175 0.5299

2 30% 0.6302 0.6267

3 21% 0.7254 0.6767

4 10% 0.8609 0.8516

MAT output as predicted mortality

• Predicted mortality closely matches observed mortality• Conventional classification differentiates mortality by range

of 0.53-0.85

PAE

A/E Actual mortality expressed as A/E ratio of 2001 VBT


Validation Results

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MAT ClassesClass % PAE A/E

1 39% 0.4766 0.46782 30% 0.6084 0.58633 21% 0.7605 0.78084 10% 1.0125 0.9130

• Once again, predicted mortality closely matches observed mortality

• MAT differentiates mortality more than Conventional– MAT range (0.47-0.91)– Conventional range (0.52-0.85)

• MAT-defined best class has mortality 10% lower than conventionally-defined best class


Epidemiological Research

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• Epidemiological research is the foundation of predication analytics for life insurance underwriting

• Selected epidemiology studies carried out by scientists at BioSignia– Predicting mortality by:

• Clinical lab tests

• Cognitive function tests

• Social factors


Clinical Lab Tests and Mortality

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• Study population

– NHANES III: 4,610 deaths occurred during the 240,428 person-year study period

– Life insurance data: 837 death claims in 1.4 million person-year study period


AST and Mortality

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NHANES III

Life Insurance

Good Predictor?


ALT and Mortality

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NHANES III

Life Insurance

Good Predictor?


GGT and Mortality

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NHANES III

Life Insurance

Good Predictor?


Blood Total Bilirubin and Mortality

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NHANES III

Life Insurance

Good Predictor?


Alkaline Phosphatase and Mortality

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NHANES III

Life Insurance

Good Predictor?


BUN and Mortality

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NHANES III

Life Insurance

Good Predictor?


Blood Creatinine and Mortality

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NHANES III

Life Insurance

Good Predictor?


Blood Albumin and Mortality

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NHANES III

Life Insurance

Good Predictor?


Cognitive Function Tests and Mortality

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• Study Population: Health Retirement Study (HRS)– 8,268 deaths occurred– 245,000 person-year study period


Word Recall

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• The interviewer read a list of 10 nouns (e.g., lake, car, army, etc.) to the respondent, and asked the respondent to recall as many words as possible from the list in any order.

% of individuals with score of <=4, by age and gender


Series 7

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• The interviewer asked the respondent to subtract 7 from 100, and continue subtracting 7 from each subsequent number for a total of five trials.

% of individuals with score of <=2 by age and gender


Age, Gender and Smoking-adjusted Hazard Ratios of WR by Age Group in HRS

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Age<60 Age 60-69

Age 70-79 Age >=80

Lower WR score, higher mortality. Impact decreases as age increases


Age, Gender, and Smoking-adjusted Hazard Ratios of SER7 by Age Group in HRS

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Age<60 Age 60-69

Age 70-79 Age >=80


Social Factors and Mortality

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• Study Population: NHANES III– 5,408 deaths occurred – 280,000 person-years study period


Education and Mortality

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• Hazard ratios of education level in age- and gender-adjusted Cox model, on all-cause mortality


Employment and Mortality

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• Hazard ratios for employment


Marital Status and Future Mortality

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• Hazard ratio for marital status


Close with Friends and Family

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• “How often do you get together with friends or relatives; I mean things like going out together or visiting in each other’s homes?”


Socialization with Neighbors

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• “How often do you visit with any of your other neighbors, either in their homes or in your own?”


Church Attendance

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• “How often do you attend church or religious services?”


Belong to any Social Organization

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• “Do you belong to any clubs or organizations such as church groups unions, fraternal or athletic groups, or school groups?”


Summary

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• Predictive analytics

– Broad fields with various applications

– Various models in the marketplace for life underwriting

• Validation of the model is difficult, but important

• Epidemiological research and literature support are the foundation of predictive analytics

Guizhou Hu, MD, PhD

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