Synergy of Machine Learning and Blockchain Strategies for Transactional Fraud Detection in FinTech Systems

Abstract

In the fast-evolving fintech ecosystem, the growing incidence of fraudulent activity presents an enormous struggle for transaction reliability. Conventional approaches to fraud detection frequently fail to keep up with the complexity of emerging fraud strategies. The ever-evolving nature of fraud necessitates intricate and adaptable solutions. This survey study evaluates and analyses current FinTech fraud detection strategies, focusing on the synergistic combination of machine learning and blockchain strategies. Reviewing the limits of existing techniques, we disclosed a high-level architecture that combines machine learning’s predictive analysis with the blockchain’s secure ledger to provide a strong defense against FinTech fraud. Contrasting conventional methodologies, this study presents a revolutionary high-level architecture that effortlessly integrates machine learning’s predictive analysis with blockchain technology, providing a cutting-edge solution to battle the ever-changing environment of financial fraud.