Advanced AI Techniques for Money Laundering Detection: A Machine Learning and Time-Frequency Analysis Approach

Abstract

Money laundering remains a significant global challenge, posing threats to economic stability and security. With advancements in artificial intelligence (AI) and machine learning (ML), financial institutions can enhance their anti-money laundering (AML) strategies. Traditional rule-based systems often generate excessive false positives and fail to adapt to evolving laundering techniques, making them inefficient. This paper presents a novel AI-driven framework for detecting suspicious activities, integrating time-frequency analysis, machine learning algorithms, and real-time detection systems. The proposed model utilizes Random Forest and Support Vector Machines (SVM) for accurate classification, reducing false positives and improving detection accuracy. By applying Fast Fourier Transform (FFT) for feature extraction, the system captures complex transactional patterns that remain undetected in conventional systems.Furthermore, the framework is designed for scalability and real-time implementation, ensuring rapid identification of suspicious behavior. Financial institutions can leverage this approach to mitigate financial crime risks, reduce operational costs, and enhance regulatory compliance. The model’s adaptability allows it to respond to emerging money laundering methods, including structuring, smurfing, and trade-based laundering. Additionally, the system provides actionable insights by analyzing patterns across multiple financial networks, contributing to the identification of criminal networks and illicit fund flows.Experimental results indicate a substantial reduction in false positive rates and a notable increase in detection accuracy compared to traditional AML approaches. Moreover, with continuous model refinement using real-time data, the system evolves to detect emerging threats effectively. By bridging the gap between regulatory requirements and technological advancements, this research contributes to the development of robust, scalable, and secure AML solutions. The proposed framework lays a strong foundation for further advancements in financial crime detection and global financial integrity protection.