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Card payment fraud is a serious problem, and a roadblock for an optimallyfunctioning digital economy, with cards (Debits and Credit) being the mostpopular digital payment method across the globe. Despite the occurrence offraud could be relatively rare, the impact of fraud could be significant,especially on the cardholder. In the research, there have been many attempts todevelop methods of detecting potentially fraudulent transactions based on datamining techniques, predominantly exploiting the developments in the space ofmachine learning over the last decade. This survey proposes a taxonomy based ona review of existing research attempts and experiments, which mainly elaboratesthe approaches taken by researchers to incorporate the (i) business impact offraud (and fraud detection) into their work , (ii) the feature engineeringtechniques that focus on cardholder behavioural profiling to separatefraudulent activities happening with the same card, and (iii) the adaptiveefforts taken to address the changing nature of fraud. Further, there will be acomparative performance evaluation of classification algorithms used andefforts of addressing class imbalance problem. Forty-five peer-reviewed paperspublished in the domain of card fraud detection between 2009 and 2020 wereintensively reviewed to develop this paper.

A Survey of Online Card Payment Fraud Detection using Data Mining-based Methods