MultiMAuS: A Multi-Modal Authentication Simulator for Fraud Detection Research

MultiMAuS is an agent-based simulator for payment transactions, intended for the analysis and development of dynamic on-line fraud detection methods via a multi-modal user authentication system. The multi-modal authentication procedure allows for a flexible number of authentication steps a user has to do before a transaction is processed (or rejected). It can thus adapt to the risk associated with a certain transaction, in the context of a given user. Our simulator is based on real-world credit card transaction data, to realistically model customer behaviour. The simulator can be used to study short and long term consequences of fraud detection algorithms, for different scenarios like varying levels of fraud or authentication steps. The implementation was done in Python, and is publicly available together with aggregated real transaction data (which serves as input to the simulator) and an example simulated transaction log.

[1]  Peter Vrancx,et al.  Game Theory and Multi-agent Reinforcement Learning , 2012, Reinforcement Learning.

[2]  Mario Zanon,et al.  Safe Reinforcement Learning Using Robust MPC , 2019, IEEE Transactions on Automatic Control.

[3]  OzturkCelal,et al.  A comprehensive survey , 2014 .

[4]  Stefan Axelsson,et al.  RETSIM: A shoe store agent-based simulation for fraud detection , 2013, ANSS 2013.

[5]  David Masad,et al.  Mesa: An Agent-Based Modeling Framework , 2015, SciPy.

[6]  Baptiste Hemery,et al.  Synthetic logs generator for fraud detection in mobile transfer services , 2013, 2013 International Conference on Collaboration Technologies and Systems (CTS).

[7]  J. Stuart Aitken,et al.  Multiple algorithms for fraud detection , 2000, Knowl. Based Syst..

[8]  Robert Michels,et al.  2016 in Review. , 2016, The American journal of psychiatry.

[9]  Javier García,et al.  A comprehensive survey on safe reinforcement learning , 2015, J. Mach. Learn. Res..

[10]  Michael L. Littman,et al.  Markov Games as a Framework for Multi-Agent Reinforcement Learning , 1994, ICML.

[11]  Maria Zhdanova,et al.  Fraud Detection in Mobile Payments Utilizing Process Behavior Analysis , 2013, 2013 International Conference on Availability, Reliability and Security.

[12]  M. R. Rao,et al.  Solution procedures for sizing of warehouses , 1998, Eur. J. Oper. Res..

[13]  Frans A. Oliehoek,et al.  A Concise Introduction to Decentralized POMDPs , 2016, SpringerBriefs in Intelligent Systems.

[14]  Leslie Pack Kaelbling,et al.  Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..

[15]  Yannis Manolopoulos,et al.  Data Mining techniques for the detection of fraudulent financial statements , 2007, Expert Syst. Appl..

[16]  Shiguo Wang,et al.  A Comprehensive Survey of Data Mining-Based Accounting-Fraud Detection Research , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[17]  S. AdewaleO.,et al.  Investigating the Effects of Threshold in Credit Card Fraud Detection System , 2012 .

[18]  Shimon Whiteson,et al.  Point-Based Planning for Multi-Objective POMDPs , 2015, IJCAI.

[19]  Nikos Vlassis,et al.  A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence I Mobk077-fm Synthesis Lectures on Artificial Intelligence and Machine Learning a Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence a Concise Introduction to Multiagent Systems and D , 2007 .

[20]  Stefan Axelsson,et al.  A review of computer simulation for fraud detection research in financial datasets , 2016, 2016 Future Technologies Conference (FTC).

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