On the Simulation of Financial Transactions for Fraud Detection Research
Licentiate THESIS AVAILABLE HERE
This thesis introduces a financial simulation model covering two related financial domains: Mobile Payments and Retail Stores systems.
The problem we address in these domains is different types of fraud. We limit ourselves to isolated cases of relatively straightforward fraud. However, in this thesis the ultimate aim is to cover more complex types of fraud, such as money laundering, that comprises multiple organisations and domains. Fraud is an important problem that impact the whole economy.
Currently, there is a general lack of public research into the detection of fraud. One important reason is the lack of transaction data which is often sensitive. To address this problem we present a Mobile Money Simulator (PaySim) and Retail Store Simulator (RetSim), which allow us to generate synthetic transactional data. These simulations are based on real transaction data.
These simulations are multi agent based simulations. Hence, we developed agents that represent the clients in PaySim and customers and salesmen in RetSim. The normal behaviour was based on behaviour observed in data from the field, and is codified in the agents as rules of transactions and interaction between clients, or customers and salesmen. Some of these agents were intentionally designed to act fraudulently, based on observed patterns of real fraud. We introduced known signatures of fraud in our model and simulations to test and evaluate our fraud detection results. The resulting behaviour of the agents generate a synthetic log of all transactions as a result of the simulation. This synthetic data can be used to further advance fraud detection research, without leaking sensitive information about the underlying data.
Using statistics and social network analysis (SNA) on real data we could calibrate the relations between staff and customers and generate realistic synthetic data sets that were validated statistically against the original. We then used RetSim to model two common retail fraud scenarios to ascertain exactly how effective the simplest form of statistical threshold detection
commonly in use could be. The preliminary results show that threshold detection is effective enough at keeping fraud losses at a set level, that there seems to be little economic room for improved fraud detection techniques.
This thesis is based on the work presented in the following four papers. The first three papers are published in peer-reviewed conference proceed-ings. Paper III obtained the Best Paper Award during the EMSS 2013 conference and we got invited to publish an extended version of this paper in a special issue of a journal. Therefore, Paper IV is currently in Press. The included papers have been modied in the format to fit this thesis format, but the content is unchanged.
Edgar Alonso Lopez-Rojas and Stefan Axelsson. Money Laundering Detection using Synthetic Data. In The 27th workshop of (SAIS), Orebro, Sweden 2012. Linkoping University Electronic Press
Edgar Alonso Lopez-Rojas and Stefan Axelsson. Multi Agent Based Simulation ( MABS ) of Financial Transactions for Anti Money Laundering ( AML ). In The 17th Nordic Conference on Secure IT Systems, Karlskrona, Sweden 2012
Edgar Alonso Lopez-Rojas, Stefan Axelsson, and Dan Gorton. Ret-Sim: A Shoe Store Agent-Based Simulation for Fraud Detection. In The 25th European Modeling and Simulation Symposium, Athens, Greece, 2013 (BEST PAPER AWARD)
Paper IV (in Press)
Edgar Alonso Lopez-Rojas, Stefan Axelsson, and Dan Gorton. Using the RetSim Simulator for Fraud Detection Research. International Journal of Simulation and Process Modelling. Special Issue in cutting edge technologies.
Invitation to defense
You are all welcome to attend Edgar Alonso Lopez-Rojas´s licentiate seminar
at the Department of Computer Science and Engineering, Blekinge Institute
Time: Monday, May 5 2014 at 12:30
Place: J1650, Campus Gräsvik, Karlskrona
Thesis title: On the Simulation of Financial Transactions for Fraud Detection Research
Research education subject: Computer Science
Examiner: Prof. Bengt Carlsson, Blekinge Institute of Technology
Main Advisor: Prof. Stefan Axelsson, Blekinge Institute of Technology
Opponent: Ph.D. Magnus Almgren, Chalmers University of Technology