Abstract

Digital banking has increased cyber dangers for financial institutions, requiring effective protection. AI and ML enable real-time fraud detection, predictive risk mitigation, and automated threat response, changing cybersecurity. Anomaly detection, behavioural analytics, and deep learning enhance fraud prevention by accurately detecting suspicious activity with few false positives. Leading banks like JPMorgan Chase and HSBC use AI-powered security solutions to detect and mitigate cyber threats in real time. Data privacy concerns, AI bias, and adversarial attacks that cause AI models to avoid discovery remain challenges. The ethical use of AI in banking security requires openness, fairness, and regulatory compliance. These issues require adaptive AI models, explainable AI (XAI), and stronger data protection policies. Quantum computing for encryption & blockchain for tamper-resistant transactions will be used in AI banking cybersecurity. Banks must develop AI models, use multi-layered authentication, and interact with regulators to improve security. Responsible AI application in financial organisations may reduce fraud, protect client data, and build digital banking confidence. This chapter examines AI-driven fraud detection methods, challenges, best practices and recommends ethical AI deployment, regulatory measures, and cybersecurity enhancements.