Ensemble Learning Framework for Money Laundering Detection

Abstract

Money laundering, which is a suspicious and critical financial activity that paves the way for illicit operations such as corruption, terrorism, and organized crime, has become widespread, undermining economic stability and regulatory systems worldwide. Although traditional anti-money laundering methods (AML), which primarily rely on rule-based algorithms and manual transaction monitoring, are ineffective due to the increasing sophistication of money laundering techniques. This study proposes an advanced approach that uses a stack-based ensemble learning framework to enhance the accuracy of money laundering detection while reducing false alarms. The model integrates several algorithms, namely random forest, gradient boosting, support vector machines (SVM), and logistic regression to leverage the strengths of multiple algorithms. Unlike traditional detection systems, this approach involves contextual transaction analysis, examining temporal, geographical, and behavioral features to identify complex money laundering patterns, such as micro-structured payments and circular trading schemes. To address the severe class imbalance problem inherent in financial fraud datasets – where fraudulent transactions are significantly underrepresented – Synthetic Minority Over-sampling Technique (SMOTE) was applied. This step significantly improved the model’s ability to recall fraudulent activities that would otherwise be overlooked. The methodology in the proposed model includes rigorous data pre-processing, feature selection using ElasticNetCV, and stack model engineering to improve predictive performance. Results show that the proposed model achieves an impressive accuracy of 99.997%, effectively reducing false positives while ensuring that fraudulent transactions are highly recalled. Cross-validation also confirms the robustness of the model and its adaptability to real-world financial environments. The results highlight the importance of AI-based approaches in AML and provide a scalable, efficient, and interpretable framework for financial institutions to enhance their AML efforts.