#PAWFINDAY1

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Prevention of Medical Abuse in
Automobile Injury Claims
By Li Yang

2. Plymouth Rock Assurance
 Automobile and homeowners insurance in Northeast US
 Where we operate
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Current
Future

3. Auto Insurance Abuse
 Premium-related
 Misrepresentations on application form: garaging address,
accident/violation history, homeowner status, etc.
 Fake insurance broker collecting premium from
unsuspecting customers
 Claims-related
 Staged car accidents
 Phantom uninsured motorist
 Exaggerated/questionable injuries
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4. Questionable Injury Example
 An insurance customer in New Jersey, “David”, has
minor car accident on his 22nd birthday
 David does not go to ER or seek any medical
treatment shortly after the accident
 Two weeks after the accident, David goes to a
chiropractor after claiming to feel pain
 No observable physical injury, just unverifiable claims of
pain
 For next six months, chiropractor treats David 3-4
times a week for 2+ hours per session
 Chiropractor also engages in suspicious billing tactics
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5. Questionable Injury Example
 David also goes to an acupuncture specialist for
additional remedies for seven months
 More questionable billing practices occur at this practice
 This pattern continues with several more medical
specialists
 After one year, David’s medical bills total $70,000
across six different medical specialists
 Under New Jersey no-fault insurance coverage,
David’s auto insurance pays the entire $70K despite
little evidence of actual injury
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6. Abuse Detection/Prevention
 Text Mining
 Anomaly Detection
 Social Media Analysis
 Predictive Modeling + Smart Claims Handling
 Graph Analytics
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7. 7
Predictive Modeling + Smart
Claims Handling

8. Handling Abusive Claims
 Abuse triaging tactics
 Request independent medical exam
 Question customer on inconsistencies
 Question medical provider on suspicious billing
 But triaging is most effective early in the claims process
 How to identify abusive claims early?
 Solution: predictive modeling
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9. Abuse Prevention System
 Construct a predictive model that can flag abusive
claims early in the claims-handling process, and then
route the claim to the appropriate handlers
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Predictive
Model
Settle
Settle
Claims
Specialists
Regular Claims
Staff
Flagged
Abusive
Not
flagged
Suspicious
Activity

10. Predictive Model
 Modeling data: accident, insurance policy, vehicle,
demographic, weather conditions, etc.
 Trained on years of historical data
 Modeling technique: Gradient Boosted Decision Tree
 Modeling software: H2O
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+ + + Prediction

11. Predictive Modeling Results
 Historical claims
 Correctly identified ~60% of abusive claims shortly after
accident was reported
 Seven-figure potential annual cost savings!
 Current claims
 Abusive billing patterns reduced by 40%
 Useful features in predictive model
 Policy tenure: longer-staying customers are less abusive
 Lag between accident and report date: longer lags are more
abusive
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12. Challenges
 For new claims, difficult to gauge model’s accuracy on a
specific claim because of intervention by claims
specialists
 Example:
• Model correctly predicts claim will be abusive
• Claim specialists successfully intervene to stop abuse, i.e. no abuse occurs
• Therefore model prediction appears to be wrong, but it’s not!
 Possible solution: A/B testing
 Some claims flagged by predictive model are not sent to
specialists, i.e. abuse is allowed to take place
 Potential costs are high
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Graph Analytics

14. Graph Analytics Overview
 Medical providers sharing the same customers can be
linked together in a network
 High counts indicate collusion because legitimate medical
providers across different medical practices rarely share >2
customers
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All Past
Claims
Chiro
David
Phys
Ther
David
David
Acup Chiro
4
customers
Phys
Ther
7
customers
5
customers
Acup

15. Example of Provider Network
 Real example of NJ network with known abusive providers
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