Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization

Some of them are related to the data distribution, notably the class imbalance of the training set (many more genuine transactions than fraudulent ones), the non-stationarity of the phenomenon (due to changes in the behavior of customers as well as in fraudsters), the large dimensionality and the overlapping classes (while fraudsters try to emulate cardholders behavior, genuine behaviors of cardholders might look strange or anomalous). The labeling process is constrained, as every day human investigators may contact only a small number of cardholders associated with the riskiest transactions and obtain the class (fraud or genuine) of the related transactions. The high cost of human labour, for assessing the transaction labels, leads to the labeling bottleneck [2]. In this context, an automatic Fraud Detection System (FDS) should support the activity of the investigators by letting them focus on the transactions with the highest fraud probability. From the perspective of the transactional service company, this is crucial in order to reduce the costs of the investigation activity and to retain the customer confidence. From a machine learning perspective it is important to keep an adequate balance between exploitation and exploration, i.e. between the short-term needs of providing good alerts to investigators, and the long-term goal of maintaining a high accuracy of the system (e.g. in the presence of concept drift). The issue of labeling the most informative data by minimizing the cost has been extensively addressed by active learning which can be considered as a specific instance of semi-supervised learning [8, 41], the domain studying how unlabeled and labeled data can both contribute to 2 Fabrizio Carcillo et al.