Fraud Detection

Travel Insurance Fraud Identification


  • A major insurance company in Singapore used to manually examine each travel insurance claim to identify potentially fraudulent one.
  • Suspicious claims were subject to a more detailed investigation.
  • The project objective was to develop a score to identify potentially fraudulent claims which would be subject to greater scrutiny.


  • Adaboost - short for Adaptive Boosting, a powerful machine learning algorithm was used for detecting potentially fraudulent cases .
  • Substantial lift demonstrated. – on the below test data set it sufficed to examine 7.75% of all claims to identify 91.67% of all fraudulent claims.


  • Data included 77,445 claim records of which only 120 had been determined to be potentially fraudulent.
  • So 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 fraudulent claims.

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