Predicting Suspicious Money Laundering Transactions using Machine Learning Algorithms

Abstract

This study employs machine learning techniques to identify key drivers of suspicious activity reporting. The data for this study comes from all suspicious activities reported to the California government in 2018. In total, there were 45,000 records of data that represent various features. The paper uses linear regression along with Lasso, Ridge, and Elastic Net to perform feature regularization and address overfitting with the data. Other probabilistic and non-linear algorithms, namely, support vector machines, random forests, XGBoost, and CatBoost, were used to deal with the complexity of the data. The results from the mean squared and root mean squared errors indicate that the ensemble tree-based algorithm performed better than the statistical and probabilistic models. The findings revealed that filings from regulators, the type of products, and customers' relationships with the institutions were the top contributors to SAR filings. Through the evaluation of a vast amount of data, this study provides valuable insights for identifying suspicious activities in financial transactions and has the potential to significantly improve suspicious transaction monitoring.

Technological Advancements and Legal Challenges to Combat Money Laundering: Evidence from Pakistan

“…Therefore, it is important to use human expertise in combination with machine learning to ensure that potential cases of money laundering are thoroughly investigated and verified. Additionally, there are legal and ethical concerns that must be addressed when using machine learning in the fight against money laundering, such as data privacy and bias in algorithmic decision-making (Lokanan & Maddhesia, 2023). This research proposes that the time has come for Pakistani financial institutions to use machine learning models.…”