A customized classification algorithm for credit card fraud detection. (June 2018) - de Sá, Alex G.C.; Pereira, Adriano C.M.; Pappa, Gisele L.
Abstract: This paper presents Fraud-BNC, a customized Bayesian Network Classifier (BNC) algorithm for a real credit card fraud detection problem. The task of creating Fraud-BNC was automatically performed by a Hyper-Heuristic Evolutionary Algorithm (HHEA), which organizes the knowledge about the BNC algorithms into a taxonomy and searches for the best combination of these components for a given dataset. Fraud-BNC was automatically generated using a dataset from PagSeguro, the most popular Brazilian online payment service, and tested together with two strategies for dealing with cost-sensitive classification. Results obtained were compared to seven other algorithms, and analyzed considering the data classification problem and the economic efficiency of the method. Fraud-BNC presented itself as the best algorithm to provide a good trade-off between both perspectives, improving the current company's economic efficiency in up to 72.64%. Graphical abstract: Highlights: We use a hyper-heuristic to create Fraud-BNC, a customized classification algorithm. Fraud-BNC is generated considering a real credit card fraud detection problem. Fraud-BNC's model has the main advantage of being interpretable by decision markers. Fraud-BNC is evaluated using both classification and economical measures. The algorithm improved the current company's economical efficiency in up to 72.64%.