An advanced blockchain-based hyperledger fabric solution for tracing fraudulent claims in the healthcare industry

Blockchain and Machine Learning (ML) are state-of-the-art technologies in the digital era. Developing countries have witnessed great digital transformation, and one of the key challenges during this development phase has been to create a platform that integrates the health and vitals of citizens confidentially. The healthcare insurance industry is one sector that has experienced a significant number of instances of fraudulent claims and mismanagement of patient data. These issues often arise due to inadequate technological integration and an over-reliance on manual processes and human intervention. The insurance industry relies on multiple processes between end users to initiate, maintain, and close diverse policies. The proposed model initially recommends a suitable insurance policy for newly admitted patients, but in the case of existing patients, the objectives are to speed up transaction processing and payment settlement securely using a private blockchain. Collectively, these two technologies possess the potential to revolutionize the future. This study aims to incorporate blockchain and ML techniques like Support Vector Machine (SVM) and Random Forest Regression, which can differentiate between fraudulent and legal medical records to recommend personalized policies, streamline claim processing, and ensure the security of sensitive patient information and vital insurance records. The key aim is to create a more patient-centric environment with data transparency. This integrated framework results in a secure, adaptable, and efficient ecosystem that outperforms traditional methods, paving the way for the future of healthcare and insurance services.