Journal of Emerging Trends in Engineering and Applied Sciences: Research Areas

Article Title: Integrating Generative AI in Fraud Detection and Anti-Money Laundering: A Comprehensive Review and Framework Proposal
by Robinson Onajite Otubu

Abstract:
The integration of Generative Artificial Intelligence (AI) into financial systems has emerged as a transformative approach to combating fraud and money laundering. With the ever-evolving sophistication of illicit financial activities, traditional detection methods are increasingly insufficient. This study provides a comprehensive review of existing applications of Generative AI in fraud detection and anti-money laundering (AML) processes, highlighting its potential to revolutionize these critical domains. Generative AI models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are explored for their ability to identify complex patterns, generate synthetic data for anomaly detection, and improve predictive accuracy. The review synthesizes key advancements, challenges, and ethical considerations surrounding Generative AI adoption in financial security. Furthermore, the study proposes a novel framework designed to optimize the deployment of Generative AI in fraud detection and AML strategies. This framework emphasizes seamless integration with existing financial systems, real-time monitoring capabilities, and robust safeguards against model vulnerabilities. The proposed approach seeks to enhance operational efficiency, minimize false positives, and bolster financial institutions' ability to stay ahead of emerging threats. By addressing the intersection of AI innovation and financial security, this research underscores the transformative potential of Generative AI while advocating for a balanced and responsible implementation. The findings aim to guide policymakers, technologists, and financial stakeholders in leveraging AI-driven solutions to protect global financial systems.

Keywords: Generative Artificial Intelligence, Fraud Detection, Anti-Money Laundering (AML), Generative Adversarial Networks (GANs), Financial Security Framework

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