Methodology
Healthcare Provider Fraud Detection
- Identifier
AYENI-2026-0019 - Version2
- Published2026-05-30
- AccessTier 1: public
Description #
An expanded methodology paper behind provider level claims anomaly detection: a supervised learning study comparing Random Forest, Support Vector Machine, and K Nearest Neighbors classifiers on structured Medicare claims data, at a fraud prevalence of about 9 percent. The best configuration, a Random Forest with a depth of 25 and 1,000 estimators, reached 86.88 percent accuracy, an F1 score of 0.8187, and an AUC ROC of 0.94. This version presents the full study as a complete paper: end to end data preprocessing, clinically informed feature engineering including chronic condition indexing and derived survival status, deployment considerations for a programme integrity unit, and the regulatory framework a detector operates inside, the False Claims Act, the Anti Kickback Statute, the Stark Law, and HIPAA.
Files #
| Path | Size | SHA-256 |
|---|---|---|
files/healthcare-provider-fraud-detection.pdf |
231,733 bytes | 75646a5ae57b0f845f58395ab3e5e07ce4c19914c1a9daaff4e4467520f17a04 |
Each file's SHA-256 is listed above. To confirm a download is unmodified: shasum -a 256 filename
Licence: CC BY 4.0