Sirvi Autor "Malkovski, Anton" järgi

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Machine Learning Methods in Anti-Money Laundering
(Tartu Ülikool, 2025) Malkovski, Anton; Aktas, Kadir, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Anti-Money Laundering (AML) is a critical operation in the financial sector, and with the constant growth in transaction volume, traditional AML methods are no longer sufficient for effectively detecting and preventing money laundering activities. Machine learning (ML) has the potential to discover complex patterns within the vast amounts of transactional data and reduce the false positives (FP) in the AML alerts. This thesis analyzes the applicability of machine learning in AML and proposes a full training pipeline that covers model training, hyperparameter optimization, and synthetic data generation. The work focuses on training a machine learning model with focus on reducing FP noise while being able to classify true positive (TP) alerts by augmenting the highly imbalance training dataset with synthetically generated minority class samples using a Variational autoencoder (VAE) and applying Optuna hyperparameter optimization to tune the model. The results of the experiments demonstrate that this method improves the model’s performance while maintaining its ability to generalize to unseen data, finally achieving noise reduction of FP alerts by 40%.