REAL-TIME INTELLIGENT CLAIM VERIFICATION USING BLOCKCHAIN-BASED RPA AND XGBOOST FOR SCALABLE HEALTHCARE FRAUD DETECTION

Healthcare insurance fraud has been a major problem and requires smart, scalable, and auditable detection systems. The study presents a framework of Robotic Process Automation (RPA) with Integrated Blockchain technology and integrates the methods of detecting fraudulent claims in real-time with Machine Learning (ML) and RPA. The system is developed in five layers, including data input, automation, intelligence, smart contracts, and blockchain ledger. It works on a structured dataset of 4,000 healthcare claims having 83 attributes. Claim intake and rule-based screening are automated by RPA bots, and fraud classification against predefined risk thresholds is done by an ML engine driven by extreme gradient boosting using smart contracts, with final results being impartially stored by Hyperledger Fabric. The proposed framework significantly outperformed the traditional models of predicting data (Convolutional Neural Network, Recurrent Neural Network, Decision Trees, and Logistic Regression) with a result of 92.4 percent of classification accuracy. It guarantees scalability and traceability without compromising the latency by making claims in under 2.5 seconds. Measurements of feature importance, e.g. KullbackLeibler Divergence, Entropy, and Gini, are more explainable and guide automation logic. What this hybrid framework will achieve is a new standard of real-time, audible, and intelligent fraud detection in healthcare insurance systems that will provide a technically sound and legally compliant basis of foundation to next-generation claims processing.