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      Dataset shift quantification for credit card fraud detection

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          Abstract

          Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However purchase behaviour and fraudster strategies may change over time. This phenomenon is named dataset shift or concept drift in the domain of fraud detection. In this paper, we present a method to quantify day-by-day the dataset shift in our face-to-face credit card transactions dataset (card holder located in the shop) . In practice, we classify the days against each other and measure the efficiency of the classification. The more efficient the classification, the more different the buying behaviour between two days, and vice versa. Therefore, we obtain a distance matrix characterizing the dataset shift. After an agglomerative clustering of the distance matrix, we observe that the dataset shift pattern matches the calendar events for this time period (holidays, week-ends, etc). We then incorporate this dataset shift knowledge in the credit card fraud detection task as a new feature. This leads to a small improvement of the detection.

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          Fraud detection system: A survey

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            Sequence classification for credit-card fraud detection

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              Feature engineering strategies for credit card fraud detection

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                Author and article information

                Journal
                17 June 2019
                Article
                1906.06977
                e11323b8-f344-4167-be83-0c681766a3c5

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                Presented at IEEE Artificial Intelligence and Knowledge Engineering (AIKE 2019)
                cs.LG cs.AI stat.ML

                Machine learning,Artificial intelligence
                Machine learning, Artificial intelligence

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