Abstract
Widespread use of Internet also had the substantial impact on the increase of the online card transactions especially with the beginning of the last decade. Along with the increase of online transactions, the worldwide banking sector was forced to deal with or to encounter an unforeseen number of fraudulent activities, yet. Hence, rule-based systems were designed to mark the high-risk transactions and let the experts to confirm the fraudulent nature of such transactions. As a countermeasure, static nature of rule-based systems were exploited by the latest attacks to go undetected. Thus, researchers aimed at designing adaptive fraud detection systems utilizing mainly machine learning techniques with the very recent application of deep learning. However, they were focused on detecting fraudulent activities but, to the best of our knowledge, none of them delved into the better understanding the characteristics of fraudulent card transactions in order to produce more resilient models. Therefore, in this study, we built the biggest data set ever used in a research, consisting of 4B non-fraud and 245K fraud transactions contributed to by the 35 banks in Turkey. Consequently, we introduce and examine the performance of profile-based fraud detection models, namely card-type based model, transaction characteristics based model, and amount-based model. Also, we made temporal and spatial analysis on our data set to show the robustness of the proposed models against aging and zero-day attacks.
Highlights
The number of credit card transactions are increasing in line with technological developments and the rise of e-commerce
In this study, we completed a comprehensive analysis on the biggest data set, namely Bankalararası Kart Merkezi (BKM) data set, ever used in fraud detection domain
Unlike most of the research work cited in the literature, we chose to generate fraud detection models according to predefined profile types
Summary
The number of credit card transactions are increasing in line with technological developments and the rise of e-commerce. The data sets used in the publications in which these results are published do not always correspond to the real banking environment in terms of numbers, characteristics, and changes in time. In this paper, unprecedented analysis on a real data set were conducted to reveal the unidentified characteristics of fraud detection activities. The resulting models were shown to have negligible effect on the fraud detection performance when compared to similar models applied to the unprofiled data set. The performance of fraud detection models using transactional characteristics were shown to decay with time. Such models require periodical training with recent fraud and non-fraud instances.
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