AI-Driven Cybersecurity in Banking: Leveraging Technology for Proactive Threat Management
Ralph A. Young · Productivity Press eBooks · 2025
Financial institutions face escalating cyber threats, with fraud losses projected to exceed $40 billion annually by 2027 (Javelin Strategy & Research, 2023 ). Traditional rule-based fraud detection systems are no longer sufficient against sophisticated attacks. Artificial Intelligence (AI) and Machine Learning (ML) offer advanced capabilities to detect, prevent, and respond to fraud in real-time while improving cybersecurity resilience. This chapter explores how banks can leverage AI/ML for fraud detection, provides real-world examples, and outlines best practices for cyber leaders. As the banking sector has always been a prime target for cybercriminals due to the sensitive nature of the data it handles and the financial gains that can be realized through successful attacks. As cyber threats become increasingly sophisticated, traditional cybersecurity measures are no longer sufficient to protect financial institutions. This chapter also delves into the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing cybersecurity within the banking sector. We explore various applications of AI, including fraud detection and prevention, behavioral analytics for transaction monitoring, AI-powered Security Operations Centers (SOCs), and automated threat hunting. Additionally, we address the ethical concerns associated with the use of AI in banking security and provide case studies that illustrate the successful implementation of AI in financial services.