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Predictive Modeling in Underwriting


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Presentation given at the 2015 SOA Annual Meeting by Barry Senensky

Published in: Business

Predictive Modeling in Underwriting

  1. 1. Predictive  Modeling  in   Underwriting BARRY  SENENSKY  FSA,  FCIA,  MAAA 14  Oct  2015 1
  2. 2. Agenda 1. Why  has  it  taken  so  long? 2. Predictive  Modeling  Approaches 3. Data  Sources 4. Building  the  Predictive  Model 5. Summary 2
  3. 3. Why  has  it  taken  so  long?
  4. 4. A  long  time  coming… Predictive  Modeling  has  been  used  in  Industry  for  50+   years Predictive  Modeling  has  been  used  by  P&C  Insurers  for   20+  years Predictive  Modeling  has  been  used  for  scoring  Disability   Claims  on  the  likelihood  of  recovery  for  over  10  years So  why  not  Underwriting? 1. Life  Insurance  business  is  conservative/slow  to  change 2. Results  take  5-­‐10  years  to  become  apparent 4
  5. 5. So  why  now? Availability  of  Data  and  CPU’s  to  process  the  data Fits  well  with  Online  Insurance  Sales  where   companies  are  looking  for  less  expensive,  less   intrusive  and  quicker  ways  to  sell  insurance  policies Just  makes  too  much  sense 5
  6. 6. Predictive  Modeling   Approaches 6
  7. 7. Predictive  Modeling  Approaches 1. Replicate  Current  Underwriting  Decisions 2. Model  mortality  rates  directly  for  unique   applicants 7
  8. 8. Data  Sources 8 1. Internal 2. Third  Party 3. Customer’s  own      
  9. 9. Internal  Data  Sources 1. Data  Collected  from  current  underwriting  practices 2. Application • Provides  good  underwriting  information   • May  have  material  inaccuracies   3. Fluids  and  other  medical  tests/information • Provides  good  underwriting  information   • Slow  and  expensive  to  collect   • Poor  customer  experience 9
  10. 10. Third  Party  Data   Includes  data  about  an  individual  obtained  from  a   third  party  including  data;   • Purchased  from  data  aggregator  such  as   LexisNexis • Purchased  from  another  company  that  has  the   individual  as  a  customer  such  as  a  pharmacy  or   telecommunications  provider • Scraped  off  the  web  such  as  from  Linked  in  or   Facebook   10
  11. 11. Third  Party  Data Advantages • Quick  to  obtain   • Low  cost • Physically  Non-­‐invasive   Concerns   • Reliability  and  completeness  of  data • Customer  Privacy   11
  12. 12. Customers’  Own  Data • Includes  data  collected  from  EHR’s,  wearable   devices  and  wellness  programs • Early  indications  are  positive  for  Auto  Insurance   • Skeptical  of  value  in  near  future  for  Life  Insurance   Underwriting 12
  13. 13. Building  the  Predictive   Model
  14. 14. Two  Possible  Approaches 1. Replicate  current  underwriting  decisions 2. Model  mortality  rates  directly  for  unique   individuals 14
  15. 15. Replicate  Current  Underwriting   Decisions   Possible  Objectives • Enhance  consistency  of    decisions  between  underwriters • Identify  predictive  data  fields • Replace  existing  process  with  one  that  is  quicker  cheaper Advantages • Historical  experience  is  not  required • Fairly  straightforward  to  develop Issues • Maintains  but  does  not  improve  underwriting  decisions • Issue  of  how  to  keep  current  over  time 15
  16. 16. Modeling  Mortality  Rates  Directly Objectives • Identify  predictive  data  fields • Replace  existing  process  with  one  that  is  quicker  and   cheaper • Predict  applicant  specific  mortality  rates Advantages • Should  improve  underwriting  decisions  and  profitability   of  business Issues • Need  historical  experience  for  all  applicants   • How  do  you  get  vital  status?   • Many  modeling  issues 16
  17. 17. Modeling  Mortality  Rates  Directly-­‐ Modeling  Issues • Build  a  model  from  scratch  or  start  with  a  standard   table?   • How  many  years  from  issue  do  we  model?  Then   what? • How  do  we  incorporate  mortality  improvement?   17
  18. 18. Smaller  Company  Issues • Accumulating  large  enough  data  sets  to  build   credible  models • Higher  unit  cost  of  building  infrastructure 18
  19. 19. Ongoing  Management • Need  to  periodically  refresh  models • Predictive  models  are  good  at  assessing  benefits  of   questions  on  applications  and  medical  tests 19
  20. 20. Summary 20
  21. 21. Summary • Predictive  Modeling  in  Underwriting  has  arrived   • If  you  haven’t  done  so  yet; ØNeed  to  decide  how  you  want  to  incorporate  into  your   underwriting  process   Øidentify  and  start  collecting  relevant  data   21
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