Title: Synergising machine learning and blockchain for enhanced fraud detection

Authors: Pawan Whig; Rattan Sharma

Addresses: Vivekananda Institute of Professional Studies-TC, New Delhi – 110034, India ' Centre for Sustainable Development, Vivekananda Institute of Professional Studies-TC and Delhi School of Business, New Delhi-110034, India

Abstract: The convergence of blockchain technology and machine learning represents a powerful paradigm shift in revolutionising fraud detection within the financial sector. This abstract highlights the synergistic potential of combining these two cutting-edge technologies, emphasising their collective impact on bolstering fraud detection and prevention strategies. Through the utilisation of blockchain's inherent features, such as transparency, immutability, and real-time monitoring, in conjunction with the predictive capabilities of machine learning, including exploratory data analysis (EDA), XGBoost, and random forest (RF), our research has achieved an outstanding accuracy rate of approximately 99.9% in fraud detection. This fusion empowers the identification of anomalies in real-time, the issuance of proactive alerts, and the development of adaptable models that continuously evolve to address emerging fraud patterns. Furthermore, the decentralised and collaborative nature of blockchain facilitates secure data sharing and leverages collective intelligence, further enhancing the precision of fraud detection. The profound implications of this integration empower financial institutions to significantly elevate transaction security, effectively combat fraudulent activities, and foster greater trust in the ever-evolving digital financial landscape.

Keywords: blockchain; fraud detection; fraud prevention; financial security; digital transactions; decentralised ledger; data integrity; real-time monitoring; patterns; fraudulent activity.

DOI: 10.1504/IJCVR.2025.146292

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.3, pp.269 - 285

Received: 26 Aug 2023
Accepted: 15 Nov 2023
Published online: 19 May 2025 *

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