Journal of Data Science and Modeling

Credit-Card Fraud Detection: Cost-Sensitive Meta-Learning Bayesian Network Classifiers

Document Type : Research Manuscript

Authors

Vahid Rezaei Tabar 1
Mohaddeseh Safakish 2

1 Department of Statistics, Faculty of Statistics, Mathematics and Computer Sciences, Allameh Tabataba'i University, Tehran, Iran

2 Department of Statistics, Faculty of Statistics, Mathematics and Computer Sciences, Allameh Tabataba’i University, Tehran, Iran.

https://doi.org/10.22054/jdsm.2025.84487.1064
Abstract
In the modern era, detecting credit card fraud has become a crucial concern from both financial and security standpoints. Given the rarity of fraudulent activities, the issue is reframed as a binary classification challenge, tackling the complexities of imbalanced datasets. To address this, authors advocate using Bayesian networks due to their theoretical robustness and capacity to model intricate scenarios while maintaining interpretability in the context of class skewed distributions. A pivotal component of this meta learning framework is the cost matrix, leading authors to explore various techniques for its calculation. By employing our meta-learning framework with data from Iran’s banking system, the authors demonstrate a method for determining the cost matrix. Subsequently, develop the corresponding Cost Augmented Bayesian Network Classifiers, called CABNCs. The outcomes highlight the potential of CATAN to diminish financial loss and the effectiveness of CAGHC-K2 in predicting labels for forthcoming transactions in the context of class imbalance.

Keywords

Bayesian network
Classification
Cost-sensitive
Economic efficiency
Fraud detection
Meta-learning
Subjects

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