Synthetic Data Generation Using Generative AI to Combat Identity Fraud and Enhance Global Financial Cybersecurity Frameworks

Abstract

Financial fraud has evolved into a complex global threat, with identity-based fraud emerging as one of its most challenging forms. The rapid advancement of generative AI provides new opportunities to address these threats by enhancing fraud prevention and detection mechanisms. This paper examines the use of synthetic data generation powered by generative AI to combat identity fraud and strengthen global financial cybersecurity frameworks. Key applications include simulating fraud scenarios to improve detection algorithms, countering synthetic identity fraud, mitigating account takeover attacks, and enhancing identity verification through biometrics. The integration of advanced models such as Generative Adversarial Networks (GANs), Conditional GANs, Variational Autoencoders (VAEs), and Transformers is explored to demonstrate their effectiveness in fraud detection, anomaly identification, and phishing communication analysis. Additionally, this paper addresses ethical considerations, regulatory challenges, and the importance of cross-border collaboration in deploying generative AI solutions for financial fraud mitigation. By highlighting these advancements, the paper provides a comprehensive overview of how generative AI can revolutionize global financial security while navigating associated risks and complexities.