Healthcare Insurance Fraud Detection Using Machine Learning and Deep Learning

Abstract: Healthcare insurance fraud causes significant financial losses and is difficult to detect due to the large volume and complexity of healthcare claims. This paper presents an integrated fraud detection framework using beneficiary, inpatient, outpatient, and provider-level fraud-label data. The datasets are cleaned, integrated, and transformed into provider-level features, with 558,211 inpatient and outpatient claim records forming the unified claims dataset. XGBoost, Random Forest, and TabNet are employed to identify providers associated with potential fraudulent activity, while class imbalance is addressed during model development. A local Llama 3.2-based explanation layer generates human-readable explanations, and a SHA-256 hash-chained audit ledger provides tamper-evident records of detection activities. A dashboard is also used to present prediction results and model performance. The proposed framework integrates fraud detection, explainability, and secure auditing for effective healthcare insurance fraud analysis.

Keywords: Healthcare Insurance Fraud Detection, TabNet, XGBoost, Random Forest, Ensemble Learning, Explainable Artificial Intelligence (XAI), Llama 3.2, SHA-256 Hash-Chained Audit Ledger.