Abstract
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%.
Highlights
Credit card fraud increases as ecommerce becomes more prevalent. [1] According to Robertson [2], global credit card fraud losses increased from $7.6 billion in 2010 to $21.81 billion in 2015
The programs of Cuckoo Search Algorithm (CS)-Support Vector Machine (SVM) algorithm were written by MATLAB R2017
This paper uses the cuckoo search algorithm to optimize the parameters of SVM to improve the classification performance of SVM
Summary
Credit card fraud increases as ecommerce becomes more prevalent. [1] According to Robertson [2], global credit card fraud losses increased from $7.6 billion in 2010 to $21.81 billion in 2015. Credit card fraud increases as ecommerce becomes more prevalent. [1] According to Robertson [2], global credit card fraud losses increased from $7.6 billion in 2010 to $21.81 billion in 2015. By 2020, global credit card fraud losses are expected to reach $31.67 billion. Credit card fraud detection methods are divided into two categories: supervised and unsupervised. In the supervised fraud detection method, models are estimated based on samples of fraud and legitimate transactions, and new transactions are classified as fraudulent or legal. Outliers or unusual transactions are identified as potential fraudulent transaction cases. Both methods of fraud detection can predict the likelihood of fraud in any given transaction [3]. Support Vector Machines [4], Logistic Regression [5], Random Forest [6], Naive Bayes [7] and Manuscript received November 18, 2019; revised August 14, 2020
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