Predictive  Modeling  in  UnderwritingBARRY  SENENSKY  FSA,  FCIA,  MAAA14  Oct  2015

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Agenda

1. Why  has  it  taken  so  long?2. Predictive  Modeling  Approaches3. Data  Sources4. Building  the  Predictive  Model5. Summary

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Why  has  it  taken  so  long?

A  long  time  coming…Predictive  Modeling  has  been  used  in  Industry  for  50+  yearsPredictive  Modeling  has  been  used  by  P&C  Insurers  for  20+  yearsPredictive  Modeling  has  been  used  for  scoring  Disability  Claims  on  the  likelihood  of  recovery  for  over  10  yearsSo  why  not  Underwriting?1. Life  Insurance  business  is  conservative/slow  to  change2. Results  take  5-­‐10  years  to  become  apparent

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So  why  now?

Availability  of  Data  and  CPU’s  to  process  the  dataFits  well  with  Online  Insurance  Sales  where  companies  are  looking  for  less  expensive,  less  intrusive  and  quicker  ways  to  sell  insurance  policiesJust  makes  too  much  sense

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Predictive  Modeling  Approaches

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Predictive  Modeling  Approaches

1. Replicate  Current  Underwriting  Decisions2. Model  mortality  rates  directly  for  unique  

applicants

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Data  Sources

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1. Internal

2. Third  Party

3. Customer’s  own      

Internal  Data  Sources

1. Data  Collected  from  current  underwriting  practices2. 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

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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  

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Third  Party  Data

Advantages• Quick  to  obtain  • Low  cost• Physically  Non-­‐invasive  

Concerns  • Reliability  and  completeness  of  data• Customer  Privacy  

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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

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Building  the  Predictive  Model

Two  Possible  Approaches

1. Replicate  current  underwriting  decisions2. Model  mortality  rates  directly  for  unique  

individuals

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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

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Modeling  Mortality  Rates  DirectlyObjectives• 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

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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?  

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Smaller  Company  Issues

• Accumulating  large  enough  data  sets  to  build  credible  models• Higher  unit  cost  of  building  infrastructure

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Ongoing  Management

• Need  to  periodically  refresh  models• Predictive  models  are  good  at  assessing  benefits  of  questions  on  applications  and  medical  tests

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Summary

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Summary

• Predictive  Modeling  in  Underwriting  has  arrived  • If  you  haven’t  done  so  yet;

ØNeed  to  decide  how  you  want  to  incorporate  into  your  underwriting  process  

Øidentify  and  start  collecting  relevant  data  

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