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External Data in UW

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Session 6 from Acceptiv Online Distribution Conference Sept 2017.

A digital sales journey requires immediate underwriting decisions made by a machine. Learn how external data can add value and how to maximize immediate decisions without devaluing the underwriting quality.

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External Data in UW

  1. 1. External Data in Underwriting Kevin Pledge CEO and Founder Acceptiv Inc. Acceptiv Confidential
  2. 2. Traditional Application Paper based, but may be converted to fillable PDF Open-ended questions 2
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  4. 4. Reflexive questioning 4 No Yes No Yes No Yes No Yes No Yes Have you been told that glaucoma is due to another disease? Term: TI CI: TI WP: TI ADB: TI Are you awaiting an operation or any investigations? Term: +0 CI: Remove Blindess WP: D ADB: +0 Has the glaucoma been treated? Is your vision now normal in the affected eye or completely corrected by spectacles or contact lenses? Did you have glaucoma in both eyes? Term: +0 CI: Remove Blindess WP: D ADB: +0 Term: +0 CI: Remove Blindess WP: D ADB: +0 Term: +0 CI: Remove Blindess WP: D ADB: +0 Term: +0 CI: +50 WP: +100 ADB: +0 Decision tree rules Closed questions, i.e. Ask a question, then a) make a decision b) ask another question Continue until a decision is made. Data has high sparsity.
  5. 5. Now we have Digital Data Can we enhance this with external data? Can we replace blood with data? 5
  6. 6. Digital Tools Available Traditional Tools Modern Tools Paramed visit Medical Tests MIB Rx MVR Behavior Science Personal External Data (incl. MIB, Rx) Non-personal External Data Behavior Science Personal External Data (incl. MVR) Non-personal External Data 6 Risk Medical Disclosure Other Risks
  7. 7. External Personal Data – (1) MIB (2) MVR (3) Rx (4) Credit Score Data (5) External non-personal data e.g. Acxiom, Environics, Cleanlist (6) Photo (7) Web fraud 7Acceptiv confidential Assessment Added value – benefit and cost Customer friendly – does it disrupt the purchase process? Integration – automated or manual? Dominant – widely accepted or exclusive?
  8. 8. MIB Not 24 – 7 Complex definitions Feedback required Can’t be used to make a decision 8 Match algorithm Protect against multiple applications? Identify non-disclosure … but widely used
  9. 9. MVR Conclusion*: Breakeven assuming MVR cost US $9 ~$40k Face Amount MVR All cause Mortality – RGA 2012 9 MVR Severity Number of Deaths Average Age Relative Mort Ratio Clean Record 50,949 43.4 95.4% Minor Violations 13,977 38.7 93.7% Major Violations 8,189 35.4 170.7% Major Violations by age: 141% - 183% Minor violations by number of violations: # Violations 1 2 3 4 5 6 7 8+ Relative Mort Ratio 95% 95% 113% 129% 138% 153% 175% 193%
  10. 10. Rx Not available in Canada In US: Does not cover all prescriptions. Difficult to integrate, but worth the effort. 10
  11. 11. Credit Score Data Uses data collected for credit scoring 11 Added value – cost per transaction (not per issued policy)
  12. 12. Photo from Lapetus 12 While traditional underwriting can take up to a month, CHRONOS delivers more accurate results in seconds Used by
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  32. 32. Picture does not seem to affect the results 36
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  34. 34. External non-personal data Likely smoker Likely enjoy outdoor activity Likely travel a lot 39 Can you use it for assessment? What version of the question should I ask?
  35. 35. Online Identity Black listed IP addresses Name check Low cost - useful at start of application, but not critical 40
  36. 36. Ask the question maybe supplement with external data Summary 41
  37. 37. Kevin Pledge CEO and Founder Acceptiv Inc. Thank You 42

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