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

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.

[1]  Millán Arroyo,et al.  Agent-based modelling and simulation for the analysis of social patterns , 2008, Pattern Recognit. Lett..

[2]  Tomas Salamon.  Design of Agent-Based Models , 2011 .

[3]  Agus Sudjianto,et al.  Statistical Methods for Fighting Financial Crimes , 2010, Technometrics.

[4]  Bianca Zadrozny,et al.  Outlier detection by active learning , 2006, KDD '06.

[5]  Philip S. Yu,et al.  Applying data mining in investigating money laundering crimes , 2003, KDD '03.

[6]  David J. Hand,et al.  Statistical fraud detection: A review , 2002 .

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  Bridget Rosewell,et al.  Validation and Verification of Agent-Based Models in the Social Sciences , 2009, EPOS.

[9]  Stefan Axelsson,et al.  Money Laundering Detection using Synthetic Data , 2012 .

[10]  Zengan Gao,et al.  A framework for data mining‐based anti‐money laundering research , 2007 .

[11]  Dan Magnusson.  The costs of implementing the anti‐money laundering regulations in Sweden , 2009 .

[12]  Stephen R. Garner,et al.  WEKA: The Waikato Environment for Knowledge Analysis , 1996 .

[13]  Sean Luke,et al.  MASON: A Multiagent Simulation Environment , 2005, Simul..

[14]  J. Ross Quinlan,et al.  Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..

[15]  Erland Jonsson,et al.  Synthesizing test data for fraud detection systems , 2003, 19th Annual Computer Security Applications Conference, 2003. Proceedings..

[16]  Kim-Kwang Raymond Choo,et al.  Modelling of money laundering and terrorism financing typologies , 2012 .

[17]  Chao-Hsien Chu,et al.  A Review of Data Mining-Based Financial Fraud Detection Research , 2007, 2007 International Conference on Wireless Communications, Networking and Mobile Computing.