Multi Agent Based Simulation (MABS) of Financial Transactions for Anti Money Laundering (AML)

Abstract

Mobile money is a service for performing financial transactions using a mobile phone. By law it has to have protection against money laundering and other types of fraud. Research into fraud detection methods is not as advanced as in other similar fields. However, getting access to real world data is difficult, due to the sensitive nature of financial transactions, and this makes research into detection methods difficult. Thus, we propose an approach based on a Multi-Agent Based Simulation (MABS) for the generation of synthetic transaction data. We present the generation of synthetic data logs of transactions and the use of such a data set for the study of different detection scenarios using machine learning.