Research on Credit Card Fraud Detection Model Based on Class Weighted Support Vector Machine

2011 ◽  
Vol 6 (1) ◽  
pp. 62-68 ◽  
Author(s):  
Qibei Lu ◽  
Chunhua Ju
Keyword(s):  
Support Vector Machine ◽  
Credit Card ◽  
Fraud Detection ◽  
Support Vector ◽  
Credit Card Fraud ◽  
Detection Model ◽  
Model Based

scholarly journals Credit Card Fraud Detection Using Weighted Support Vector Machine

2020 ◽  
Vol 11 (12) ◽  
pp. 1275-1291
Author(s):  
Dongfang Zhang ◽  
Basu Bhandari ◽  
Dennis Black
Keyword(s):  
Support Vector Machine ◽  
Credit Card ◽  
Fraud Detection ◽  
Support Vector ◽  
Credit Card Fraud

scholarly journals Application of Credit Card Fraud Detection Based on CS-SVM

2021 ◽  
Vol 11 (1) ◽  
pp. 34-39
Author(s):  
Chenglong Li ◽  
◽  
Ning Ding ◽  
Haoyun Dong ◽  
Yiming Zhai ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Credit Card ◽  
Naive Bayes ◽  
Search Algorithm ◽  
Fraud Detection ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Credit Card Fraud

With the development of e-commerce, credit card fraud is also increasing. At the same time, the way of credit card fraud is also constantly innovating. Support Vector Machine, Logical Regression, Random Forest, Naive Bayes and other algorithms are often used in credit card fraud identification. However, the current fraud detection technology is not accurate, and may cause significant economic losses to cardholders and banks. This paper will introduce an innovative method to optimize the support vector machine by cuckoo search algorithm to improve its ability of identifying credit card fraud. Cuckoo search algorithm improves classification performance by optimizing the parameters of support vector machine kernel function (C, g). The results demonstrate that CS-SVM is superior to SVM in Accuracy, Precision, Recall, F1-score, AUC, and superior to Logistic. Regression, Random Forest, Decision Tree, Naive Bayes, whose accuracy is 98%.

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