The rapid advancement of artificial intelligence (AI) and machine learning (ML) has significantly transformed fraud detection and cybersecurity in the banking sector. Traditional rule-based security mechanisms are no longer sufficient to combat the evolving landscape of cyber threats, financial fraud, and identity theft. AI-powered fraud detection systems leverage machine learning algorithms, anomaly detection, and predictive analytics to identify suspicious activities in real-time, reducing financial losses and enhancing security. Furthermore, AI-driven cybersecurity solutions, including behavioral biometrics, threat intelligence, and automated response mechanisms, play a crucial role in safeguarding sensitive financial data. This chapter explores the integration of AI in banking fraud detection and cybersecurity, discussing various AI techniques such as supervised and unsupervised learning, deep learning, and reinforcement learning. Additionally, we examine case studies and real-world implementations to highlight the effectiveness of AI in detecting fraud, mitigating cyber risks, and ensuring regulatory compliance. The chapter concludes with future directions, emphasizing the need for continuous AI model improvements, ethical considerations, and collaboration between financial institutions and regulatory bodies to enhance banking security in the digital era.