Machine Learning · Big Data · 2026
Healthcare Fraud Detection System
Real-time ML inference, Medallion data pipeline and an explainable enterprise dashboard.
92.5%
Model accuracy
94.8%
ROC AUC
44+
API endpoints
27
Dashboard pages
The problem
Healthcare insurers lose large amounts to fraudulent claims that are hard to spot manually. Reviewers need not only a risk score, but a clear reason why a claim looks suspicious.
The solution
A production-grade platform that scores every claim with an XGBoost model trained on 20K+ synthetic claims, explains each decision with SHAP feature contributions, and surfaces everything in a role-based dashboard for insurers and providers.
Medallion data architecture
Data flows through three curated layers so every metric is reproducible.
- Bronze — raw synthetic health claims ingested as-is
- Silver — cleaned, deduplicated and validated records
- Gold — aggregated business metrics: claims per patient, average cost, fraud cases
Machine learning
- XGBoost classifier with 24 engineered features
- Real-time scoring on claim submission
- SHAP explanations per claim with severity levels and confidence scores
- Model management: metrics, version history and retraining triggers
Dashboard experience
- 22 insurance pages: executive KPIs, analytics, flagged claims, fraud heatmap, audit logs
- 3 provider pages: submit claims with instant ML scoring and track statuses
- Alert center, notifications, reports with CSV export and system monitoring
Results & impact
- ✓92.5% accuracy and 94.8% ROC AUC on held-out claims
- ✓Every flag is explainable, so reviewers can justify a decision
- ✓Geographic and provider-level views reveal fraud clusters, not just single claims
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