  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.
