Self-learning Fraud Intelligence Engine for Blockchain-based Payment Networks Using Multi-modal Machine Learning

Abstract

The rapid adoption of blockchain-based payment networks has introduced new challenges in securing digital transactions against sophisticated fraud schemes. Traditional fraud detection mechanisms often struggle with the volume, velocity, and heterogeneity of blockchain transaction data. This study proposes a Self-Learning Fraud Intelligence Engine leveraging multi-modal machine learning to detect and adapt to evolving fraudulent behaviors in blockchain payment systems. The engine integrates transaction history, user behavioral patterns, and network-level data to generate a comprehensive fraud risk profile in real time. By employing adaptive learning algorithms, the system continuously refines its detection models, improving accuracy while minimizing false positives. Experimental simulations on representative blockchain payment scenarios demonstrate significant enhancements in fraud detection performance compared to conventional rule-based and static machine learning approaches. The findings underscore the potential of self-learning, multi-modal AI frameworks in fortifying blockchain payment networks, enabling faster, more reliable, and fraud-resilient financial transactions.