Social Simulation of Commercial and Financial Behaviour for Fraud Detection Research

We present a social simulation model that covers three main financial services: Banks, Retail Stores, and Payments systems. Our aim is to address the problem of a lack of public data sets for fraud detection research in each of these domains, and provide a variety of fraud scenarios such as money laundering, sales fraud (based on refunds and discounts), and credit card fraud. Currently, there is a general lack of public research concerning fraud detection in the financial domains in general and these three in particular. One reason for this is the secrecy and sensitivity of the customers data that is needed to perform research. We present PaySim, RetSim, and BankSim as three case studies of social simulations for financial transactions using agent-based modelling. These simulators enable us to generate synthetic transaction data of normal behaviour of customers, and also known fraudulent behaviour. 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 can calibrate the relations between staff and customers, and generate realistic synthetic data sets. The generated data represents real world scenarios that are found in the original data with the added benefit that this data can be shared with other researchers for testing similar detection methods without concerns for privacy and other restrictions present when using the original data.