A customized classification algorithm for credit card fraud detection

Alex Guimarães Cardoso de Sá, Adriano C. M. Pereira, Gisele L. Pappa

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%. 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%.