Anti-Money Laundering Compliance Using Feature Engineering with SQL Analytics, TF-IDF and Oversampling: Conditional Tabular Generative Adversarial Networks
Pub. online: 25 June 2025
Type: Research Article
Open Access
Open Access
Received
1 March 2025
1 March 2025
Accepted
1 June 2025
1 June 2025
Published
25 June 2025
25 June 2025
Abstract
Traditional Anti-Money Laundering (AML) systems rely on rule-based approaches, which often fail to adapt to evolving money laundering tactics and produce high false-positive rates, overwhelming compliance teams. This study proposes an innovative machine learning (ML) framework that leverages Conditional Tabular Generative Adversarial Networks (CTGANs) to address severe class imbalance, a common challenge in Suspicious Activity Reporting (SAR). Implemented in Python, CTGAN generates realistic synthetic samples to enhance minority-class representation, improving recall and F1-scores. For instance, the Random Forest (RF) model achieves a recall of 0.991 and an F1-score of 0.528 in oversampled datasets with engineered variables, highlighting the effectiveness of CTGAN in mitigating imbalance. This framework also incorporates SQL-based feature engineering using Oracle Analytics, creating dynamic variables such as cumulative sums, rolling averages, and ranks. The modelling phase and exploratory data analysis are conducted in the SAS programming language, employing Logistic Regression (LR) as baseline, Decision Trees (DT), and RF. Evaluation across undersampled and oversampled datasets, combined with varying probability thresholds, reveals key trade-offs between sensitivity and precision. Among the models, RF consistently achieves the highest ROC-AUC scores, ranging from 0.945 in undersampled datasets to 0.951 in oversampled configurations, demonstrating its robustness and accuracy in SAR detection. By integrating CTGAN and TF-IDF (textual feature transformation in Python) with SQL-engineered variables, this framework provides a comprehensive data-driven approach to AML. It reduces false positives, strengthens the detection of suspicious activities, and ensures scalability, adaptability, and compliance with regulatory standards.
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Biographies
Andreescu Anca Ioana
A.I. Andreescu graduated from the Faculty of Cybernetics, Statistics and Economic Informatics of the Academy of Economic Studies in 2001. She got the title of doctor in economy in the specialty economic informatics in 2009. At present she is an associate professor in the Department of Economic Informatics and Cybernetics of the Bucharest University of Economic Studies. Her interest domains related to computer science are requirements engineering, business analytics, modelling languages, business rules approaches and software development methodologies.
Oprea Simona-Vasilica
S.-V. Oprea received the MSc degree through the Infrastructure Management Program from Yokohama National University, Japan, in 2007, her first PhD degree in power system engineering from the Bucharest Polytechnic University in 2009, and her second PhD degree in economic informatics from the Bucharest University of Economic Studies in 2017. She is currently a professor within the Faculty of Cybernetics, Statistics, and Economic Informatics with the Bucharest Academy of Economic Studies, involved in several research projects.
Văduva Alin Gabriel
A.-G. Văduva earned his bachelor’s degree in economic informatics in 2022 and his master’s degree in databases – Support for Business in 2024. He is currently pursuing a PhD, focusing on the trustworthiness of artificial intelligence algorithms in business. Professionally, he works as an artificial intelligence engineer. His research interests include mathematics, machine learning, data mining, deep learning, and generative AI.
Bâra Adela
A. Bâra graduated the Faculty of Economic Cybernetics in 2002, holds a PhD diploma in economics from 2007. She is a professor at the Economic Informatics Department at the Faculty of Cybernetics, Statistics and Economic Informatics from The Bucharest University of Economic Studies and has coordinated three R&D projects. Her research interests are focused on data science, analytics, databases, IoT, big data, data mining, power systems, authoring more than 70 papers in international journals and conferences.
