AI-Powered Financial Forensics in Automating Anomaly Detection and AML Compliance

Geol Gladson Battu

Abstract: Financial transactions have become more complex and faster than?ever before and have put the traditional Anti-Money Laundering and forensic accounting processes under strain. The financial industry faces mounting pressure to detect and prevent fraudulent activities, money laundering, and regulatory breaches with increasing speed and accuracy. Rules based approaches frequently do not scale to the complexity and volume of?transactions experienced within the financial services industry. This paper explores how?AI and ML can be integrated into financial forensics especially automating anomaly detection and AML compliance. A spectrum of AI?models, including supervised, unsupervised, and reinforcement learning methods, are analyzed to ascertain their level of efficacy for the purpose of detecting complex hidden patterns that might signal illegitimate activities. This paper explores real-world use cases in which AI systems outperform the manual and static?rule-based systems in finding anomalies with very low false positive rates. Additionally, the paper explores the growing need for explainable AI models, data privacy and shifting?compliance requirements. Further, the paper outlines how AI-enabled financial forensics not only improves the ability to detect anomalies but improves the efficiency of the compliance process, decreases the cost of operations, and?offers measures to respond to various complex financial crimes. This study underscores AI as?the central driver of disruptive innovation towards financial surveillance and regulatory compliance.

Keywords: Artificial Intelligence, Financial Forensics, Anomaly Detection, Anti-Money Laundering, Compliance Automation, Machine Learning

How to Cite?: Geol Gladson Battu, "AI-Powered Financial Forensics in Automating Anomaly Detection and AML Compliance", Volume 14 Issue 5, May 2025, International Journal of Science and Research (IJSR), Pages: 799-806, https://www.ijsr.net/getabstract.php?paperid=SR25512195301, DOI: https://dx.doi.org/10.21275/SR25512195301