FRAUD SCORING FOR INSURANCE CLAIM

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.

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

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

• Gradient Boosting
  • 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