Integrating Blockchain with Machine Learning for Fraud Detection in Health Insurance Claims Management
Keywords:
Blockchain, Machine Learning, Fraud Detection, Health Insurance, Claims Management, Healthcare Technology, Data Security, Anomaly Detection, Predictive Analytics, System IntegrationAbstract
Fraud in health insurance claims poses a significant challenge to the modern healthcare industry, leading to substantial financial losses and undermining the trust in health insurance systems. This research paper introduces an innovative approach to fraud detection in health insurance claims by integrating blockchain technology with machine learning algorithms. The blockchain framework provides a secure, transparent, and immutable ledger for health insurance data, while machine learning models enhance the detection of fraudulent patterns and anomalies within claims data. This study outlines a comprehensive methodology, detailing the design, development, and implementation of a blockchain-based system for managing health insurance claims, integrated with advanced machine learning techniques for fraud detection. The mathematical foundations underpinning the proposed models are rigorously detailed, and the system's performance is evaluated through extensive experimental analysis. Our findings indicate that the integrated approach significantly improves the accuracy and reliability of fraud detection in health insurance claims, offering a valuable solution for stakeholders in the healthcare sector.
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