A DECENTRALIZED MACHINE LEARNING APPROACH FOR FRAUD DETECTION WITH BLOCKCHAIN-DRIVEN PRIVACY PROTECTION

Abstract

Modern digital ecosystems have the significant difficulty of detecting fraud while managing large, real-time data transfer and privacy preservation. This study presents an innovative architecture that combines blockchain technology with machine learning to provide safe, transparent, and privacy-conscious fraud detection. The solution utilizes federated learning and differential privacy methods to train machine learning models without revealing raw user data, while using blockchain's decentralized framework to guarantee data immutability and reliability. A dynamic incentive framework using smart contracts further motivates users to provide detection-ready, high-quality data. The suggested method promotes cooperation among entities, protects user data security, and achieves enhanced fraud detection accuracy via the integration of privacy-preserving computing and decentralized trust. Experimental assessments on both simulated and actual financial datasets illustrate the system's precision, robustness, and scalability in detecting intricate and evolving fraud patterns.