Enhancing Fraud Detection in Financial Transactions: A Machine Learning and Blockchain Integration Framework

As technology continues to transform the landscape of financial transactions, the need for effective fraud detection systems has become increasingly critical. This paper explores the synergistic integration of machine learning (ML) and blockchain technology to enhance the efficacy of fraud detection mechanisms. Using a comprehensive dataset of Ethereum transactions, we propose an innovative framework that leverages advanced ML algorithms—specifically XGBoost and Random Forest—combined with the secure and decentralized features of the blockchain’s immutable ledger. Our findings show that this integration significantly improves both detection accuracy and operational efficiency, with XGBoost emerging as the most effective algorithm in this context. The inherent immutability of blockchain technology bolsters the pattern recognition capabilities of ML, resulting in a robust solution for real-time fraud detection. This research not only underscores the potential of combining these two cutting-edge technologies but also paves the way for future advancements in secure financial transactions.

کلیدواژه ها:

Fraud Detection ، Machine Learning ، Blockchain Technology ، Ethereum Transactions ، Secure Financial Transactions ، Integration Framework (key words)