Authors: Edgar A. Lopez-Rojas; Dan Gorton; Stefan Axelsson
Addresses: Department of Computer Science and Engineering, Blekinge Institute of Technology, Karlskrona, Sweden ' Center for Safety Research, Department of Transport Science, KTH Royal Institute of Technology, Stockholm, Sweden ' Department of Computer Science and Engineering, Blekinge Institute of Technology, Karlskrona, Sweden
Abstract: Managing fraud is important for business, retail and financial alike. One method to manage fraud is by detection, where transactions, etc. are monitored and suspicious behaviour is flagged for further investigation. There is currently a lack of public research in this area. The main reason is the sensitive nature of the data. Publishing real financial transaction data would seriously compromise the privacy of both customers, and companies alike. We propose to address this problem by building RetSim, a multi-agent-based simulation (MABS) calibrated with real transaction data from one of the largest shoe retailers in Scandinavia. RetSim allows us to generate synthetic transactional data that can be publicly shared and studied without leaking business sensitive information, and still preserve the important characteristics of the data. We then use RetSim to model two common retail fraud scenarios to ascertain exactly how effective the simplest form of statistical threshold detection could be. The preliminary results of our tested fraud detection method show that the threshold detection is effective enough at keeping fraud losses at a set level, that there is little economic room for improved techniques.
Keywords: privacy; anonymisation; multi-agent simulation; MABS; ABS; retail stores; fraud detection; synthetic data; RetSim simulator; multi-agent systems; agent-based systems; fraud management; shoe retailers; agent-based modelling; synthetic transactional data; public sharing; data sharing; retail fraud; fraud losses .
International Journal of Simulation and Process Modelling, 2015 Vol.10 No.2, pp.144 - 155
Received: 24 Jan 2014
Accepted: 29 Jul 2014
Published online: 07 Jul 2015 *