Social Simulation of Commercial and Financial Behaviour for Fraud Detection Research
2014 (English)In: Advances in Computational Social Science and Social Simulation / [ed] Miguel, Amblard, Barceló & Madella, Barcelona, 2014Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
Privacy; Anonymization; Multi-Agent-Based Simulation; MABS; ABS; Retail Store; Fraud Detection; Synthetic Data
IdentifiersURN: urn:nbn:se:bth-12931OAI: oai:DiVA.org:bth-12931DiVA: diva2:954146
Social Simulation Conference. Bellaterra, Cerdanyola del Valles, 1a : 2014