<p>As financial fraud schemes grow increasingly sophisticated, traditional detection models struggle to keep pace with the evolving threat landscape. Generative Artificial Intelligence (AI), particularly models like Generative Adversarial Networks (GANs) and Large Language Models (LLMs), are emerging as transformative tools in the realm of fraud detection. These models enable the creation of synthetic datasets, simulate fraudulent behaviors, and enhance the accuracy of anomaly detection systems. By generating realistic fraud scenarios, generative AI enhances predictive modeling and supports proactive risk mitigation strategies in financial institutions. However, the use of generative AI also raises critical concerns around data privacy, explainability, and potential misuse. This paper explores the current and future applications of generative AI in fraud detection, outlines the regulatory and ethical considerations, and offers forward-looking recommendations for integrating these tools into secure, transparent, and efficient fraud risk management frameworks.</p>