Predicting Money Laundering using Machine Learning and Artificial Neural Networks Algorithms in Banks

Abstract

This paper aims to build a machine learning and a neural network model to detect the probability of money laundering in banks. The paper's data came from a simulation of actual transactions flagged for money laundering in Middle Eastern banks. The main findings highlight that criminal networks mainly use the integration stage to integrate money into the financial system. Fraudsters prefer to launder funds in the early hours, morning followed by the business day's afternoon time intervals. Additionally, the Naïve Bayes and Random Forest classifiers were identified as the two best-performing models to predict bank money laundering transactions.

The effect of the banking supervision on anti-money laundering

“…Money laundering is the process of converting the proceeds of criminal activities into clean money that appears to come from a legitimate source. In other words, it puts money obtained from illegal sources into the natural financial cycle or into the process of money circulation within the economy by disguising governments, and making them appear as clean money, and using them for various purposes (Lokanan, 2022).…”