Blockchain-Based Fraud Detection System for Healthcare Insurance Claims

Abstract

Healthcare fraud is a huge concern that affects not only the financial viability of insurance companies but also the well-being of patients who may receive compromised care due to fraudulent acts. Addressing this issue demands novel solutions that can detect and prevent fraudulent conduct in healthcare insurance claims. The project intends to establish an automated fraud detection system using blockchain technology, which has advantages such as security, transparency, and data immutability. By leveraging blockchain's decentralized ledger, the system creates a tamper-proof platform for processing healthcare insurance claims, preventing fraudulent alterations and enhancing trust in the integrity of the claims process. Ethereum's blockchain platform and smart contracts play a critical role in ensuring the secure recording of transactions while preventing retroactive alterations. Moreover, an on-chain database is employed to manage relevant claim data, thereby safeguarding its integrity and ensuring accessibility. The decentralized nature of blockchain technology brings additional advantages by eliminating the need for intermediaries, thereby reducing administrative costs and streamlining the claim processing workflow. The adoption of methodologies such as Personal Extreme Programming (PXP) and Design Science Research Methodology (DSRM) fortifies the project's framework. PXP facilitates continuous improvement through incremental and iterative development, while DSRM ensures a structured approach to problem-solving, yielding reliable results. Through rigorous testing and validation, the automated fraud detection system enhances the efficiency and accuracy of fraud identification in healthcare insurance claims. By combining blockchain technology with methodological frameworks, this project offers a promising solution to combat healthcare fraud, safeguarding insurance systems' integrity and ensuring quality care for patients. Future iterations will focus on expanding the system's capabilities and refining its algorithms to counter the evolving fraudulent tactics prevalent in the healthcare industry.