Healthcare Insurance Fraud Detection Powered by Blockchain and Machine Learning: An Analysis and Framework

Abstract

Targeting the increasing incident of fraudulent ac-tivities in health care claims processing, this article introduces Machine Learning (ML) with blockchain technology The im-mediate importance of this effort is to improve the security and reliability of the medical claims process to support the equitable and efficient distribution of funds. The main objective of this research is to develop and implement fraud detection and prevention operations using the machine learning algorithms and blockchain technology. Through the integration of these two technologies, our efforts to create a private and protective environment for the processing of medical data reduce the risk of fraud. Our approach is based on the integration of the Ethereum blockchain to increase the power of complex machine learning models. Regarding the use of blockchain, we use Ganache as a private blockchain built by Ethereum, powered by Pinata service to securely store data. We also use machine learning models to analyze personal insurance practices and identify potential fraud. This amalgamation of technologies heralds a paradigm shift in fraud detection and prevention, fostering the preservation of healthcare data integrity and equitable allocation of resources while ensuring minimal losses for insurance companies.