Engineering and Technology
| Open Access |
DOI: Integrating Blockchain Security and Machine Learning for Fraud Detection in the U.S. Banking System
Mohammad Musa Mia , Master of Business Administration, International American University, Los Angeles, California Molay Kumar Roy , Ms in Digital Marketing & Information Technology Management, St. Francis College, USA I K M SAAMEEN YASSAR , Masters of Science and Information Technology, Washington University of Science and Technology, USA Md Yassir Mottalib , Master of Science in Information System Technology, Wilmington University, USA Syed Yezdani , Master’s in computer science, Saint Leo University, Tampa, Florida. Alifa Majumder Nijhum , MS of Information Technology Project Management, St Francis College, USA Rumana Shahid , Department of Management Science and Quantitative Methods, Gannon University, USA Md Kafil Uddin , Dahlkemper School of Business, Gannon University, USAAbstract
The increasing sophistication of financial fraud in the U.S. banking system requires advanced and transparent detection mechanisms. This study proposes a blockchain-enabled machine learning framework that enhances fraud detection accuracy and data integrity. Using an open-source dataset from the UCI Machine Learning Repository, five supervised models—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Neural Network—were trained and evaluated. Data preprocessing included feature scaling, encoding, and class balancing to ensure reliability and model generalization. Results show that integrating blockchain’s immutable ledger with artificial intelligence significantly improves detection performance. The Neural Network model achieved the best results with 99.1% accuracy, 98.6% precision, 98.9% recall, and a 98.7% F1-score, outperforming all other algorithms. The blockchain layer ensured data transparency, traceability, and tamper resistance throughout the detection process. This research demonstrates that combining blockchain and AI can strengthen fraud prevention, enhance regulatory compliance under U.S. financial laws, and foster greater trust in digital banking operations. The proposed system offers a scalable and secure foundation for the next generation of fraud detection in the U.S. financial sector.
Keywords
Blockchain, fraud detection, U.S. banking, machine learning, neural network, cybersecurity
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Copyright (c) 2025 Mohammad Musa Mia, Molay Kumar Roy, I K M SAAMEEN YASSAR, Md Yassir Mottalib, Syed Yezdani, Alifa Majumder Nijhum, Rumana Shahid, Md Kafil Uddin
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