FRAUD SCORING FOR INSURANCE CLAIM

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TRAVEL INDUSTRY

  • Almost 10% of global GDP can be attributed to travel and hospitality industry
  • Fraudsters are naturally attracted to a sector with high monetary value, rapid growth and large amount of online and digital transactions
  • Various travel frauds arise from fake travel agents, transaction with stolen cards and reselling in grey market, sham website, insurance fraud etc.
  • $5.7 trillion spent in travel and hospitality industry in 2021

TRAVEL INSURANCE FRAUD

  • Fabricated trip cancellation when the real reason is not covered by insurance
  • False claim for lost baggage
  • Exaggerating the value of items in allegedly lost baggage
  • Fictitious medical treatment supposedly received during a trip
  • ~20% of travel claims are fraudulent

PREVENTION OF INSURANCE FRAUD

  • Each insurance claim is scrutinized one by one and if possible,  compare with the previous claim history of the insured. 
  • Artificial intelligence and machine learning tools are widely adopted which  are trained over lots of data to look for anomalies of potentially fraudulent activity patterns. The methodologies used are
    • Outlier detection
    • Classification
    • Clustering
  • They consider a variety of features that a human may miss

Case Study

  • A Singapore based travel insurance company used to manually scrutinize each claim to detect potentially fraudulent ones
  • The process involved pre specified rule as well as judgment
  • Suspicious claims were subject to more detailed investigation
  • The investigating practice involved many hours of manual work and inconsistence process
Objective
  • Focus on reduced number of claims and thus 
  • reduce the manual work in initial scrutiny
  • while significantly improving fraud detection
Challenges
  • Data included 77,445 claim records of which only 120 had been determined to be potentially fraudulent 
  • Identified potentially fraudulent claims are rare events (0.15%) and therefore hard to detect 
  • It was however expected that there could be a large number of undetected fake claims
Solution
  • A powerful machine learning algorithm
  • Each claim was scored reflecting the risk of fraud
  • Higher the score higher was the probability of the claim being fraudulent
Benefit
  • Process automation
         Bypass manual scrutiny
  • Efficiency
         Save time and effort
  • Increased accuracy
         Eliminate human error
  • Cost saving
         reducing  manual work + false claims


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