Journal
JOURNAL OF COMPUTATIONAL SCIENCE
Volume 27, Issue -, Pages 247-254Publisher
ELSEVIER
DOI: 10.1016/j.jocs.2018.06.009
Keywords
Credit card fraud detection; Bagging; Risk-based modeling; Bayesian inference; Weighted voting
Funding
- DEITY
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Credit card fraud represents one of the biggest threats for organizations due to the probability of huge losses associated with them. This paper presents a cost-sensitive Risk Induced Bayesian Inference Bagging model, RIBIB, for credit card fraud detection. RIBIB proposes a novel bagging architecture incorporating a constrained bag creation method, a Risk Induced Bayesian Inference method as a base learner and a cost-sensitive weighted voting combiner. Experiments on Brazilian Bank data indicate 1.04-1.5 times reduced cost. Experiments on UCSD-FICO data exhibit robustness of the model in handling unseen data without any need for domain specific parameter fine-tuning. (C) 2018 Elsevier B.V. All rights reserved.
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