Edgar Alonso Lopez-Rojas

Using the RetSim simulator for fraud detection research

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 f ...

Using the RetSim Fraud Simulation Tool to Set Thresholds for Triage of Retail Fraud

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.

Analysis of fraud controls using the PaySim financial simulator

Fraud controls for financial transactions are needed and required by law enforcement agencies to flag suspicious criminal activity. These controls however require deeper analysis of the effectiveness and the negative impact for the legal customers. Owing to the intrinsically private nature of financial transactions this analysis is often performed after several months of actively using fraud controls. In this paper, we present an analysis of different fraud prevention controls on a mobile money service based on thresholds using a simulator called PaySim. PaySim uses aggregated data from a sample dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour. With technology frameworks such as agent-based simulation techniques, and the application of mathematical statistics, we show in this paper that the simulated data can be as prudent as the original dataset for setting optimal controls for fraud detection.

A review of computer simulation for fraud detection research in financial datasets

The investigation of fraud in the financial domain has been restricted to those who have access to relevant data. However, customer financial records are protected by law and internal policies, therefore they are not available for most of the researchers in the area of fraud detection. This paper aims to present the work of those researchers who have had access to data and present an interesting approach to fraud detection research; which is the generation of a synthetic data set to work on fraud detection research. Some of the domains covered in this review include mobile money payments, e-payments, retail stores, online bank services and credit card payments. We also cover some of the most relevant surveys in the field and point out the impossibility to compare this work due to the lack of common public data set to test different results.