Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Financial fraud continues to evolve in scale, sophistication, and speed, rendering traditional rule-based and supervised machine learning systems increasingly inadequate. This paper presents a comprehensive analysis of how generative artificial intelligence (AI) can transform fraud detection in banking by enabling proactive, adaptive, and highly scalable defense mechanisms. It examines the capabilities of key generative architectures—including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and transformer-based Large Language Models (LLMs)—and how they enhance anomaly detection, behavioral modeling, synthetic data generation, and unstructured text analysis. Real-world case studies from Swedbank, Mastercard, and JPMorgan demonstrate measurable improvements in detection rates, reduction of false positives, and faster identification of compromised accounts. The paper also discusses architectural considerations for deploying generative models at scale, addressing challenges related to adversarial attacks, explainability, privacy, and regulatory compliance. Finally, it explores emerging directions such as multimodal fraud detection, federated learning, adversarial defenses, and quantum-enhanced AI systems. By integrating generative AI with robust governance, scalable cloud architectures, and human oversight, banks can significantly strengthen their fraud detection capabilities and stay ahead of increasingly AI-enabled financial crime.