Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6874304 | Journal of Computational Science | 2018 | 8 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Akila S, Srinivasulu Reddy U,