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Beyond Supervised Learning in Credit Card Fraud Detection: A Dive into Semi-supervised and Distributed Learning

par Carcillo, Fabrizio
Président du jury Lenaerts, Tom
Promoteur Bontempi, Gianluca
Publication Non publié, 2018-06-22

Thèse de doctorat

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Résumé :

The expansion of the electronic commerce, as well as the increasing confidence of customers in electronic payments, makes of fraud detection a critical issue. The design of a prompt and accurate Fraud Detection System is a priority for many organizations in the business of credit cards. In this thesis we present a series of studies to increase the precision and the speed of fraud detection system. The thesis has three main contributions. The first concerns the integration of unsupervised techniques and supervised classifiers. We proposed several approaches to integrate outlier scores in the detection process and we found that the accuracy of a conventional classifier may be improved when information about the input distribution is used to augment the training set.The second contribution concerns the role of active learning in Fraud Detection. We have extensively compared several state-of-the-art techniques and found that Stochastic Semi-supervised Learning is a convenient approach to tackle the Selection Bias problem in the active learning process.The third contribution of the thesis is the design, implementation and assessment of SCARFF, an original framework for near real-time Streaming Fraud Detection. This framework integrates Big Data technology (notably tools like Kafka, Spark and Cassandra) with a machine learning approach to deal with imbalance, non-stationarity and feedback latency in a scalable manner. Experimental results on a massive dataset of real credit card transactions have showed that our framework is scalable, efficient and accurate over a big stream of transactions.