Proactive Machine Learning Approach to Combat Money Laundering in Financial Sectors

Authors: Abhinav Balasubramanian

DOI: https://doi.org/10.5281/zenodo.14508474

Short DOI: https://doi.org/g8v5k8

Country: USA

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Abstract: Money laundering poses a significant challenge to the integrity of financial systems, enabling illicit activities and undermining economic stability. Traditional anti-money laundering (AML) systems often rely on rigid rule-based approaches that struggle to detect complex laundering patterns, leading to high false positive rates and inefficiencies. This paper proposes a proactive machine learning-based framework to address these limitations by leveraging advanced data analytics, anomaly detection algorithms, and adaptive learning techniques.
The proposed framework integrates supervised and unsupervised machine learning models to analyze transaction data, identify anomalies, and generate predictive insights into potential laundering activities. Designed with scalability and adaptability in mind, the system seamlessly integrates into existing financial infrastructures while ensuring adherence to data privacy standards. Simulated scenarios and case studies illustrate the framework’s potential to improve detection accuracy, reduce manual intervention, and respond dynamically to emerging laundering threats.
This paper highlights the potential of machine learning to redefine AML practices by providing a conceptual framework that lays the groundwork for future development and real-world deployment. By advancing AML strategies, the proposed approach offers a pathway toward more effective and adaptive solutions for mitigating illicit financial activities.

Keywords: Artificial Intelligence (AI), Anti-Money Laundering (AML), Machine Learning in Finance, Transaction Monitoring Systems, Predictive Analytics and adaptive learning for AML

Paper Id: 231852

Published On: 2019-03-08

Published In: Volume 7, Issue 2, March-April 2019

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