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Using the RetSim simulator for fraud detection research
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-9158-3488
KTH Royal Institute of Technology.
2015 (English)In: International Journal of Simulation and Process Modelling, ISSN 1740-2123, E-ISSN 1740-2131, Vol. 10, no 2Article in journal (Refereed) Published
Abstract [en]

Managing fraud is important for business, retail and financialalike. One method to manage fraud is by \emph{detection}, wheretransactions etc. are monitored and suspicious behaviour is flaggedfor further investigation. There is currently a lack of publicresearch in this area. The main reason is the sensitive nature of thedata. Publishing real financial transaction data would seriouslycompromise the privacy of both customers, and companies alike. Wepropose to address this problem by building RetSim, a multi-agentbased simulator (MABS) calibrated with real transaction data from oneof the largest shoe retailers in Scandinavia. RetSim allows us togenerate synthetic transactional data that can be publicly shared andstudied without leaking business sensitive information, and stillpreserve the important characteristics of the data.

We then use RetSim to model two common retail fraud scenarios toascertain exactly how effective the simplest form of statisticalthreshold detection could be. The preliminary results of our testedfraud detection method show that the threshold detection is effectiveenough at keeping fraud losses at a set level, that there is littleeconomic room for improved techniques.

Place, publisher, year, edition, pages
InderScience Publishers, 2015. Vol. 10, no 2
Keyword [en]
Privacy; Anonymization; Multi-Agent-Based Simulation; MABS; ABS; Retail Store; Fraud Detection; Synthetic Data
National Category
Computer Systems
URN: urn:nbn:se:bth-12930DOI: 10.1504/IJSPM.2015.070465OAI: diva2:954142
Available from: 2016-08-20 Created: 2016-08-20 Last updated: 2016-08-30Bibliographically approved
In thesis
1. Applying Simulation to the Problem of Detecting Financial Fraud
Open this publication in new window or tab >>Applying Simulation to the Problem of Detecting Financial Fraud
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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 introduce our approach towards the use of computer simulation for fraud detection and its applications in financial domains. Fraud is an important problem that impact the whole economy. Currently, there is a 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 Payment Simulator (PaySim) and Retail Store Simulator (RetSim), which allow us to generate synthetic transactional data that contains both: normal customer behaviour and fraudulent behaviour. 


These simulations are Multi Agent-Based Simulations (MABS) and were calibrated using real data from financial transactions. We developed agents that represent the clients and merchants 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 and merchants, 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 methods. 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 or breaking any non-disclose agreements.


Using statistics and social network analysis (SNA) on real data we calibrated the relations between our agents and generate realistic synthetic data sets that were verified against the domain and validated statistically against the original source.


We then used the simulation tools to model common fraud scenarios to ascertain exactly how effective are fraud techniques such as the simplest form of statistical threshold detection, which is perhaps the most common in use. The preliminary results show that threshold detection is effective enough at keeping fraud losses at a set level. This means that there seems to be little economic room for improved fraud detection techniques.


We also implemented other applications for the simulator tools such as the set up of a triage model and the measure of cost of fraud. This showed to be an important help for managers that aim to prioritise the fraud detection and want to know how much they should invest in fraud to keep the loses below a desired limit according to different experimented and expected scenarios of fraud.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2016
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 6
security, privacy, anonymisation, multi-agent-based simulation, MABS, ABS, retail store, fraud detection, synthetic data, mobile money
National Category
Computer Systems
urn:nbn:se:bth-12932 (URN)978-91-7295-329-1 (ISBN)
External cooperation:
Public defence
2016-10-28, J1650, Blekinge Institute of Technology, Karlskrona, 10:00 (English)
Knowledge Foundation, 20140032
Available from: 2016-08-30 Created: 2016-08-20 Last updated: 2016-09-01Bibliographically approved

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