Claims Subrogation Analytics
3AI August 15, 2020
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
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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
