Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 72, Issue -, Pages 21-29Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2018.03.011
Keywords
Credit card fraud; Bayesian network classifiers; Hyper-heuristic
Categories
Funding
- CNPq [481204/2013-0, 573871/2008-6, 459301/2014-4]
- CAPES [3479/2014]
- FAPEMIG [PPM-00650-15, APQ-01400-14]
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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 (UREA), 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%.
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