Claims Subrogation Analytics

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Leading US Health Insurance Company

Problem Statement

  • Annual revenue leakage because of subrogation non-recovery: ~ $79.2 MN
  • Subrogation recovery rate: 4 %
  • Business objective was to analyze closed subrogation cases and identify case segments having high propensity of recovery, so that focused attempt can be made to recover them

Analytics Led Approach

  • Given are high level process steps which were followed

    • Distribution analysis ; Correlation analysis ;Anova
    • Original Amount, LOS, Age, Units Billed, DX (transformed) ; # of Claim Lines
    • Test, Control split ; Logistic Regression model development; Model selection
    • False positives, true negatives comparison for cut off determination
    • Insights and business rules formulation

Business Impact

  • Predictive analytical model was designed to identify case segments having high propensity for subrogation recovery
  • A systematic approach to transform the model results into business rules, that can augment current subrogation process making it more effective was framed as well

Critical Success Factors

  • A potential recovery rate increase of 8% - 22 % for cases with >60% of subro potential
  • Opportunity to plug in revenue leakage of USD ~5 MN has been addressed
  • Additional business rules to prioritize cases for subrogation recovery that can seamlessly integrate within existing workflow

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