A Survey of Online Card Payment Fraud Detection using Data Mining-based Methods
This survey is significant for researchers and practitioners in the financial technology domain by providing a structured overview of existing methods and challenges in card payment fraud detection.
This survey paper reviews 45 peer-reviewed papers published between 2009 and 2020 on online card payment fraud detection using data mining techniques. It proposes a taxonomy elaborating approaches to incorporate business impact, feature engineering for cardholder behavioral profiling, and adaptive efforts to address changing fraud patterns.
Card payment fraud is a serious problem, and a roadblock for an optimally functioning digital economy, with cards (Debits and Credit) being the most popular digital payment method across the globe. Despite the occurrence of fraud could be relatively rare, the impact of fraud could be significant, especially on the cardholder. In the research, there have been many attempts to develop methods of detecting potentially fraudulent transactions based on data mining techniques, predominantly exploiting the developments in the space of machine learning over the last decade. This survey proposes a taxonomy based on a review of existing research attempts and experiments, which mainly elaborates the approaches taken by researchers to incorporate the (i) business impact of fraud (and fraud detection) into their work , (ii) the feature engineering techniques that focus on cardholder behavioural profiling to separate fraudulent activities happening with the same card, and (iii) the adaptive efforts taken to address the changing nature of fraud. Further, there will be a comparative performance evaluation of classification algorithms used and efforts of addressing class imbalance problem. Forty-five peer-reviewed papers published in the domain of card fraud detection between 2009 and 2020 were intensively reviewed to develop this paper.