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CAA2018 Predictive Analytics

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Opportunities to apply predictive analytics in life insurance underwriting.

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CAA2018 Predictive Analytics

  1. 1. Full Member of Caribbean Actuarial Association 28th Annual Conference Jamaica Pegasus Hotel Kingston, Jamaica 28th - 30th November 2018
  2. 2. Predictive Analytics: Case Studies in Life Insurance Kevin Pledge
  3. 3. Direct to Consumer
  4. 4. Direct to Consumer
  5. 5. Average Agent Age 57 Families are Under-insured Personal preference
  6. 6. To sell online we have to get better at Underwriting Convenience of immediate issue Consumer pays significant premium for convenience Fails the customer Simplified Issue Few easy questions to select good risks Most business still referred to full UW Fails on brand promise Accelerated Issue Price competitive immediate issue Customer needs to answer more questions InsureCo. needs to figure out how to uw without blood Immediate Issue
  7. 7. Underwriting Stack (Traditional) Personal Disclosure Vitals Fluids APS Additional Exams Traditional Analytics MIB Traditionally hand written descriptions to explain any conditions Verify truthful disclosure Diagnostic 47 days Performance sucks Customer Experience Cost
  8. 8. Qualification Process New Analytics Underwriting Stack (Accelerated) Personal Disclosure Vitals Fluids APS Additional Exams Traditional Analytics MIB Digital – ideally reflexive Verify truthful disclosure Diagnostic 7 47 days Performance mixed Customer Experience Cost
  9. 9. Behavioral Science New Analytics Underwriting Stack (Immediate) Personal Disclosure Vitals Fluids APS Additional Exams Traditional Analytics MIB Digital – reflexive Diagnostic 12 mins Performance good Customer Experience Cost -Traditional Analytics MIB Verify truthful disclosure
  10. 10. Personal Disclosure Vitals Fluids APS Additional Exams Traditional Analytics MIB Personal Disclosure New Analytics APS Traditional Analytics MIB Personal Disclosure New Analytics Traditional Analytics MIB Behavioral Science Verify truthful disclosure Diagnostic New Analytics Traditional ImmediateAccelerated
  11. 11. Traditional Analytics and MIB MVR Criminal Records RxRx 24/7 Secret codes ?
  12. 12. New Analytics – Marketing Data Too general to use for decisions, but… could modify reflexive questions
  13. 13. New Analytics – Credit Score Examples: TRL – TransUnion LexisNexis Equifax
  14. 14. New Analytics – Facial Analytics from Lapetus While traditional underwriting can take up to a month, CHRONOS delivers more accurate results in seconds
  15. 15. Let’s try it
  16. 16. 4
  17. 17. Behavioral Science Rethink how you ask questions: Have you smoked or used any substance or product containing nicotine, tobacco or marijuana in the past 12 months? If you have not smoked or used nicotine replacement products (including e-cigarettes) for over 12 months we consider you to be a non-smoker.
  18. 18. What best describes you as a non-smoker? 1. I have never smoked 2. I recently quit 3. I quite more than a year ago 4. I only smoke occasionally at social events What best describes you as a smoker? 1. I only smoke occasionally 2. I smoke less than 20 cigarettes per day 3. I smoke more than 20 cigarettes per day
  19. 19. Finally – UW Approach You don’t have to make a diagnosis to make an underwriting decision “but doctor – there must be something wrong with me, the life underwriter said so”
  20. 20. Summary Underwriting is an integrated decision Evaluate analytics objectively Cost is a factor - Don’t keep adding to the stack More info: https://www.acceptiv.com/caa2018

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