Data-Driven Methods for Machine Learning-Based Fraud Detection and Cyber Risk Mitigation in National Banking Infrastructure

Abstract

The increasing reliance on digital banking infrastructure has escalated the risks associated with cyber threats and fraudulent transactions. The integration of data-driven machine learning techniques has emerged as a pivotal approach to mitigating these risks, enhancing fraud detection capabilities, and ensuring the security of national banking systems. This paper examines the role of machine learning-based fraud detection and cyber risk mitigation by analyzing data-driven methodologies, including supervised and unsupervised learning models, deep learning architectures, and real-time anomaly detection systems. The discussion also delves into the challenges associated with data privacy, adversarial attacks, and model interpretability in banking applications. The paper proposes a structured framework for integrating machine learning models within national banking infrastructure, emphasizing the importance of model robustness, scalability, and regulatory compliance. By leveraging advanced analytics, financial institutions can proactively detect and mitigate cyber risks, safeguarding both financial assets and consumer trust. This research underscores the necessity of continuous adaptation to evolving cyber threats, advocating for a synergistic approach combining machine learning, regulatory policies, and advanced cybersecurity measures.