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Guizhou Hu, MD, PhD

Guizhou Hu, MD, PhD. Executive Vice President & Chief Scientific Officer BioSignia , Inc. Agenda. What is Predictive Analytics? Predictive Analytics in life insurance An example of predictive model in life insurance underwriting

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Guizhou Hu, MD, PhD

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  1. Guizhou Hu, MD, PhD Executive Vice President & Chief Scientific Officer BioSignia, Inc.

  2. 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

  3. 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.”

  4. Examples of Predictive Models • LinkedIn connection recommendations • NCAA Tournament bracket tools • Amazon.com product recommendations • Framingham cardiovascular model

  5. Attributes of Good Predictive Models • 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

  6. 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..)

  7. Industry Forces Shaping Adoption of Predictive Analytics

  8. 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

  9. How MAT Was Validated • 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)

  10. Validation Results PAE MAT output as predicted mortality • Predicted mortality closely matches observed mortality • Conventional classification differentiates mortality by range of 0.53-0.85 A/E Actual mortality expressed as A/E ratio of 2001 VBT

  11. Validation Results • 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

  12. Epidemiological Research • 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

  13. Clinical Lab Tests and Mortality • 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

  14. AST and Mortality NHANES III Good Predictor? Life Insurance

  15. ALT and Mortality NHANES III Good Predictor? Life Insurance

  16. GGT and Mortality NHANES III Good Predictor? Life Insurance

  17. Blood Total Bilirubin and Mortality NHANES III Good Predictor? Life Insurance

  18. Alkaline Phosphatase and Mortality NHANES III Good Predictor? Life Insurance

  19. BUN and Mortality NHANES III Good Predictor? Life Insurance

  20. Blood Creatinine and Mortality NHANES III Good Predictor? Life Insurance

  21. Blood Albumin and Mortality NHANES III Good Predictor? Life Insurance

  22. Cognitive Function Tests and Mortality • Study Population: Health Retirement Study (HRS) • 8,268 deaths occurred • 245,000 person-year study period

  23. Word Recall • 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

  24. Series 7 • 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

  25. Age, Gender and Smoking-adjusted Hazard Ratios of WR by Age Group in HRS Age 60-69 Age<60 Age >=80 Age 70-79 Lower WR score, higher mortality. Impact decreases as age increases

  26. Age, Gender, and Smoking-adjusted Hazard Ratios of SER7 by Age Group in HRS Age 60-69 Age<60 Age 70-79 Age >=80

  27. Social Factors and Mortality • Study Population: NHANES III • 5,408 deaths occurred • 280,000 person-years study period

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

  29. Employment and Mortality • Hazard ratios for employment

  30. Marital Status and Future Mortality • Hazard ratio for marital status

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

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

  33. Church Attendance • “How often do you attend church or religious services?”

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

  35. Summary • 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

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