Claims Subrogation Analytics
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
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
