Claims Fraud Detection

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Leading Global P&C Insurance Company

Problem Statement

  • Customers’ current SIU was geared towards proactively identifying and detecting claimant related frauds.
  • Fraud related to medical provider were proving to be difficult to identify and work upon
  • Customer had to rely on referrals from regulatory and industry bodies to identify suspect cases and wanted to explore ways and means to pro-actively identify provider fraud through statistical means

Analytics Led Approach

  • Framework on claims fraud detection was developed to identify the potential losses due to fraud or suspect claims and thereby helped to  reduce claims leakage to a great extent
  • Given are high level process steps which were followed
    • Sensitive data encryption
    • Load the data from multiple sources
    • Data cleansing
    • Data transformation
    • Business Significant variables deduction
    • Distribution analysis
    • Correlation analysis
    • Multi- dimensional Analysis Fraud analysis through data profiling

Business Impact

  • An analytical solution that focuses on providers rather than transactions, and looks for patterns in provider behavior was developed
  • The solution methodology was end-to-end: ingesting medical invoice data, creating its own data assets and classifying outlier behavior using unsupervised machine learning techniques
  • Solution outcomes were tangible and allow self-serve configuration of business rules.
  • Recommendations can readily be evaluated by follow-up investigation and $ business benefits are easy to estimate and establish

Critical Success Factors

  • Key suspect indicators and patterns were identified which indicated possible provider frauds
  • Identified $ 6.4 million was charged for medical bills by suspect medical Providers

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