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.

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

Main Subjects

• Bayesian Network