Implementation of Gradient Boosted Tree, Support Vector Machinery and Random Forest Algorithm to Detecting Financial Fraud in Credit Card Transactions

Ferdinand Salomo Leuwol, Asri Ady Bakri, Muhsin N. Bailusy, Hari Setia Putra, Ni Ketut Sukanti
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Abstract

According to Google Trends data, machine learning-based credit card identification has grown over the last five years, at the very least, across all nations. In order to detect credit card fraud in this study, the authors will use machine learning methods such random forests, support vector machines, and gradient-boosted trees. The authors used the Synthetic Minority Oversampling Technique (SMOTE) and Random Under Sampling (RUS) sampling methods in each algorithm to compare because there was a class imbalance in this investigation. The research findings demonstrate that the author's algorithm and sample technique were successfully used, as shown by the AUC values obtained for each being > 0.7. The top score in RUS was 0.7835 using the Random Forest algorithm, whereas the greatest score in SMOTE was 0.73 with the Gradient Boosted Trees approach. The Random Forest algorithm and the Random Under Sampling (RUS) technique are developed as a result of this research, and they are useful for identifying fraudulent credit card transactions.
梯度增强树、支持向量机和随机森林算法在信用卡交易金融欺诈检测中的实现
根据谷歌趋势数据,至少在过去五年中,基于机器学习的信用卡识别在所有国家都有所增长。为了在本研究中检测信用卡欺诈,作者将使用机器学习方法,如随机森林、支持向量机和梯度增强树。由于本研究中存在类不平衡,作者采用了合成少数过采样技术(SMOTE)和随机欠采样(RUS)两种算法进行比较。研究结果表明,作者的算法和样本技术得到了成功的应用,分别为>0.7. 使用随机森林算法,RUS的最高得分为0.7835,而使用梯度增强树方法,SMOTE的最高得分为0.73。随机森林算法和随机欠采样(RUS)技术是该研究的结果,它们对识别欺诈性信用卡交易很有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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