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Finance and Insurance

Use case: Reduction in fraud cases through machine learning analysis of structured and unstructured data

Context

Multinational insurance corporation’s compliance office experienced overwhelming amount of data in fraud investigation

  • Significant need for manual intervention to understand patterns among claims
  • FTEs wasted time on high rate of false alarms
  • Existing model was business-rules-driven and inflated claims through analyzing historical data

Objective: Reduction in fraud cases through machine learning analysis of structured and unstructured data

Approach

Machine learning used to create prioritized list of claims

System analyzed reams of structured/ unstructured data from claims databases to handwritten notes from adjusters

Machine learning identified potentially fraudulent activity to produce list of 100 claims ranked from highest to lowest priority

System produced visuals of data and connections between data points
Fraud investigators used visuals to understand connections behind numbers and improve algorithm

Impact

Solution implemented with fraud investigation unit in the field

Led to strategic approach to analyzing fraudulent claims

Effectiveness and efficiency improvements of process observed

  • Significantly reduced FTE investigation time
  • Identified cases of fraud that otherwise would not have been flagged through manual investigation

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