Machine learning and Blockchain approaches for enhancing fraud prevention in financial transactions

Financial fraud continues to threaten the integrity of digital financial systems, with traditional rule-based detection methods increasingly ineffective against evolving tactics. Recent advances suggest that integrating Machine Learning (ML) and Blockchain Technology may provide a robust solution. This study explores how this hybrid approach can improve fraud detection in financial transactions by enhancing accuracy, transparency, and response efficiency. The study adopted a quantitative research design using a publicly available dataset of over 2,500 financial transactions. Features included transaction amounts, account behavior, login attempts, and timestamps. Supervised machine learning models—Random Forest and Support Vector Machine (SVM)—were applied to classify transactions as fraudulent or legitimate. The models were trained, optimized, and evaluated using metrics such as accuracy, precision, recall, and F1-score. Descriptive analysis revealed fraud accounted for only 6.8% of transactions, confirming significant class imbalance. The Random Forest model outperformed the SVM, achieving 99.9% accuracy, precision, recall, and F1-score. TransactionAmount, TransactionDuration, and CustomerOccupation were found to be the most influential predictors. The integration of blockchain was identified as vital for secure, immutable data storage, enabling real-time auditability and enhancing the trustworthiness of the machine learning process. The combination of machine learning’s predictive power with blockchain’s immutable ledger creates a highly effective fraud detection framework. Random Forest was identified as the superior model in terms of both performance and reliability for this application. Financial institutions should adopt integrated ML-blockchain systems to strengthen fraud prevention, ensure transaction transparency, and support regulatory compliance. This study contributes to the advancement of intelligent and secure financial systems by offering empirical evidence of the value of this hybrid approach.  Keywords: Fraud Detection, Machine Learning, Blockchain, Random Forest, Financial Transactions, Cybersecurity, Anomaly Detection.