Wang Ning^1,*, Siliang Chen^2,*, Fu Qiang², Haitao Tang², Shen Jie²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5951-5965, 2023, DOI:10.32604/cmc.2023.035558

Abstract With the popularity of online payment, how to perform credit card fraud detection more accurately has also become a hot issue. And with the emergence of the adaptive boosting algorithm (Adaboost), credit card fraud detection has started to use this method in large numbers, but the traditional Adaboost is prone to overfitting in the presence of noisy samples. Therefore, in order to alleviate this phenomenon, this paper proposes a new idea: using the number of consecutive sample misclassifications to determine the noisy samples, while constructing a penalty factor to reconstruct the sample weight assignment. Firstly, the theoretical analysis shows that… More >

Yiğit Kültür, Mehmet Ufuk Çağlayan

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 807-817, 2018, DOI:10.1080/10798587.2017.1342415

Abstract Because credit card fraud costs the banking sector billions of dollars every year, decreasing the losses incurred from credit card fraud is an important driver for the sector and end-users. In this paper, we focus on analyzing cardholder spending behavior and propose a novel cardholder behavior model for detecting credit card fraud. The model is called the Cardholder Behavior Model (CBM). Two focus points are proposed and evaluated for CBMs. The first focus point is building the behavior model using single-card transactions versus multi-card transactions. As the second focus point, we introduce holiday seasons as spending periods that are different… More >

Masoumeh Zareapoor, Jie Yang

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 721-727, 2018, DOI:10.1080/10798587.2017.1321228

Abstract The design of an efficient credit card fraud detection technique is, however, particularly challenging, due to the most striking characteristics which are; imbalancedness and non-stationary environment of the data. These issues in credit card datasets limit the machine learning algorithm to show a good performance in detecting the frauds. The research in the area of credit card fraud detection focused on detection the fraudulent transaction by analysis of normality and abnormality concepts. Balancing strategy which is designed in this paper can facilitate classification and retrieval problems in this domain. In this paper, we consider the classification problem in supervised learning… More >

Yong Fang¹, Yunyun Zhang², Cheng Huang^1,*

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 185-195, 2019, DOI:10.32604/cmc.2019.06144

Abstract In recent years, the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit. Credit card transactions take a salient role in nowadays’ online transactions for its obvious advantages including discounts and earning credit card points. So credit card fraudulence has become a target of concern. In order to deal with the situation, credit card fraud detection based on machine learning is been studied recently. Yet, it is difficult to detect fraudulent transactions due to data imbalance (normal and fraudulent transactions), for which Smote algorithm is proposed in order to resolve data imbalance. The assessment of… More >