Credit card fraud is the new financial fraud crime accompanied by the gradual development of the economy which causes billions of dollars of losses every year. Credit card fraud case not only seriously violated the cardholder benefits and financial institutions, but also undermined the credit management order. However, fraudsters keep exploring new crime strategies constantly which exacerbates the crime rate of fraud. Thus, a predictive model for credit card fraud detection is essential to minimize its losses. By distinguishing between fraud and non-fraud, machine learning is one of the most efficient solutions for detecting fraud. Support vector machines have proven to be a novel algorithm with excellent performance. Nevertheless, the performance of SVM depends largely on the correct choice of model parameters (C and g), which could cause that the false positive was very high if the kernel function type and parameter cannot be selected properly. In this paper, based on the real transaction data of the credit card business, firstly, it will find the optimal kernel function suitable for the data set. Secondly, this paper will propose the method of optimizing the support vector machine parameters by the cuckoo search algorithm, genetic algorithm and particle swarm optimization algorithm. Last but not least, the Linear kernel function was found to be the best kernel function with an accuracy rate of 91.56%. Furthermore, the Radial basis function is used to optimize the kernel function, which can improve the accuracy from 42.86% to the highest accuracy rate of 98.05%. Compared with CS-SVM and GA-SVM, PSO-SVM has the best overall performance.

中文翻译：

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基于不同支持向量机的信用卡欺诈检测比较研究

信用卡欺诈是伴随着经济的逐步发展而引起的新的金融欺诈犯罪，每年造成数十亿美元的损失。信用卡欺诈案不仅严重侵犯了持卡人的利益和金融机构，而且破坏了信用管理秩序。但是，欺诈者不断探索新的犯罪策略，这加剧了欺诈的犯罪率。因此，信用卡欺诈检测的预测模型对于最大程度地减少其损失至关重要。通过区分欺诈和非欺诈，机器学习是检测欺诈的最有效解决方案之一。支持向量机已被证明是一种具有出色性能的新颖算法。尽管如此，SVM的性能很大程度上取决于模型参数（C和g）的正确选择，如果无法正确选择内核函数类型和参数，则可能导致误报率很高。本文基于信用卡业务的真实交易数据，首先找到适合该数据集的最优核函数。其次，提出了通过布谷鸟搜索算法，遗传算法和粒子群算法对支持向量机参数进行优化的方法。最后但并非最不重要的一点是，发现线性核函数是最佳核函数，准确率为91.56％。此外，径向基函数用于优化内核函数，可以将精度从42.86％提高到98.05％的最高准确率。与CS-SVM和GA-SVM相比，PSO-SVM具有最佳的整体性能。