Enhancing Medical Billing Transparency with Blockchain and Random Forest Based Fraud Detection

The healthcare sector remains in struggling with fraudulent billing methods and insufficient transactional transparency, resulting in financial inefficiencies and diminished stakeholder confidence. This paper presents a hybrid system that combines blockchain technology with a Random Forest (RF)-based fraud detection algorithm to tackle these difficulties. Blockchain is utilized to provide the security, immutability, and transparency of medical billing information through decentralized data storage and the provision of verifiable audit trails. Built upon this secure architecture, the RF method is employed to identify irregularities in billing patterns that may signify fraudulent activity. Experimental findings on a labelled dataset indicate the model's exceptional efficacy, attaining an accuracy of 97.5%, precision of 97.97%, recall of 97%, and an F1-score of 97.48%. These findings validate the model's efficacy in detecting fraudulent transactions while preserving a minimal false positive rate. The proposed system provides a scalable and reliable solution for increasing transparency, reducing fraud, and promoting overall efficiency in healthcare billing processes.