RetSim is an agent-based simulator of a shoe store basedon the transactional data of one of the largest retail shoesellers in Sweden. The aim of RetSim is the generationof synthetic data that can be used for fraud detection re-search. Statistical and a Social Network Analysis (SNA)of relations between staff and customers was used to de-velop and calibrate the model. Our ultimate goal is forRetSim to be usable to model relevant scenarios to gen-erate realistic data sets that can be used by academia, andothers, to develop and reason about fraud detection meth-ods without leaking any sensitive information about theunderlying data. Synthetic data has the added benefit ofbeing easier to acquire, faster and at less cost, for exper-imentation even for those that have access to their owndata. We argue that RetSim generates data that usefullyapproximates the relevant aspects of the real data.

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.

We present a social simulation model that covers three main financialservices: Banks, Retail Stores, and Payments systems. Our aim is toaddress the problem of a lack of public data sets for fraud detectionresearch in each of these domains, and provide a variety of fraudscenarios such as money laundering, sales fraud (based on refunds anddiscounts), and credit card fraud. Currently, there is a general lackof public research concerning fraud detection in the financial domainsin general and these three in particular. One reason for this is thesecrecy and sensitivity of the customers data that is needed toperform research. We present PaySim, RetSim, and BankSim asthree case studies of social simulations for financial transactionsusing agent-based modelling. These simulators enable us to generatesynthetic transaction data of normal behaviour of customers, and alsoknown fraudulent behaviour. This synthetic data can be used to furtheradvance fraud detection research, without leaking sensitiveinformation about the underlying data. Using statistics and socialnetwork analysis (SNA) on real data we can calibrate the relationsbetween staff and customers, and generate realistic synthetic datasets. The generated data represents real world scenarios that arefound in the original data with the added benefit that this data canbe shared with other researchers for testing similar detection methodswithout concerns for privacy and other restrictions present when usingthe original data.

RetSim is a multi-agent based simulator (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.

In this paper we extended the fraud model of RetSim to cover more cases of internal fraud perpetrated by the staff and allow inventory control to flag even more suspicious activity. We also generated sufficient number of runs using a range of fraud parameters to cover a vast number of fraud scenarios that can be studied. We then use RetSim to simulate some of the more common retail fraud scenarios to ascertain exactly the cost of fraud using different fraud parameters for each case.

The investigation of fraud in business has been a staple for the digital forensics practitioner since the introduction of computers in business. Much of this fraud takes place in the retail industry. When trying to stop losses from insider retail fraud, triage, i.e. the quick identification of sufficiently suspicious behaviour to warrant further investigation, is crucial, given the amount of normal, or insignificant behaviour. It has previously been demonstrated that simple statistical threshold classification is a very successful way to detect fraud~\cite{Lopez-Rojas2015}. However, in order to do triage successfully the thresholds have to be set correctly. Therefore, we present a method based on simulation to aid the user in accomplishing this, by simulating relevant fraud scenarios that are foreseeing as possible and expected, to calculate optimal threshold limits. This method gives the advantage over arbitrary thresholds that it reduces the amount of labour needed on false positives and gives additional information, such as the total cost of a specific modelled fraud behaviour, to set up a proper triage process. With our method we argue that we contribute to the allocation of resources for further investigations by optimizing the thresholds for triage and estimating the possible total cost of fraud. Using this method we manage to keep the losses below a desired percentage of sales, which the manager consider acceptable for keeping the business properly running.