Bayesian Quickest Detection of Credit Card Fraud

Abstract

This paper addresses the risk of fraud in credit card transactions by developing a probabilistic model for the quickest detection of illegitimate purchases. Using optimal stopping theory, the goal is to determine the moment, known as disorder or fraud time, at which the continuously monitored process of a consumer's transactions exhibits a disorder due to fraud, in order to return the best trade-off between two sources of cost: on the one hand, the disorder time should be detected as soon as possible to counteract illegal activities and minimize the loss that banks, merchants and consumers suffer; on the other hand, the frequency of false alarms should be minimized to avoid generating adverse effects for cardholders and to limit the operational and process costs for the card issuers. The proposed approach allows us to score consumers' transactions and to determine, in a rigorous, personalized and optimal manner, the threshold with which scores are compared to establish whether a purchase is fraudulent.

Trends, impacts and responses to cybercrime in the Zimbabwean retail sector

“…In what seems to be at variance with the cybercrime trends, where card fraud has been regarded as the major cyber threat to the retail business by previous studies (Mugari and Olutola, 2021; Buonaguidi, 2019; SABRIC, 2018; KPMG, 2015), findings from this study reveal that card fraud is not as prevalent as threats such as virus dissemination and hacking. This may possibly be explained by the fact that payment cards are widely used in the developed countries than in the developing countries.…”

“…Longstanding research has identified card fraud as one of the dominant forms of cybercrime affecting the retail sector and this is largely due to the adoption of payment cards as the major electronic payment system (Mugari and Olutola, 2021; Buonaguidi, 2019; SABRIC, 2018; KPMG, 2015). According to Mugari and Olutola (2021), card fraud comes in four types, namely, card not present fraud, counterfeit card fraud, lost or stolen card fraud and application fraud.…”

Unsupervised Insurance Fraud Prediction Based on Anomaly Detector Ensembles

“…In the finance industry the prediction of credit card fraud (cf. Buonaguidi et al 2022), stock market anomalies and insurance claim fraud (cf. Gomes et al 2021) are the most common examples of anomaly detection (see Phua et al 2010 for a survey of fraud detection).…”