Risks of Digital Transformation: Review of Machine Learning Algorithms in Credit Card Fraud Detection

Güneş Gürsoy, A. Varol
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Abstract

In addition to the advantages of the digital world, there are also disadvantages, which can harm people. With the spread of credit cards with the digital transformation, banks have become the targets of malicious hackers. In this study, firstly, information about artificial intelligence and digital transformation is given. In related studies, some machine learning methods such as Random Forest, Naive Bayes, K-Nearest Neighbor, Logistic Regression, Support Vector Machines, Decision Tree, Artificial Neural Networks, Multilayer Perceptron and Ensemble Learning have been used to detect credit card fraud and their algorithm performance has been demonstrated.
数字化转型的风险:信用卡欺诈检测中的机器学习算法综述
除了数字世界的优点,也有缺点,这可能会伤害人们。随着信用卡在数字化转型中的普及,银行成为了恶意黑客攻击的目标。在本研究中,首先给出了人工智能和数字化转型的相关信息。在相关研究中,随机森林、朴素贝叶斯、k近邻、逻辑回归、支持向量机、决策树、人工神经网络、多层感知机和集成学习等机器学习方法已被用于信用卡欺诈检测,并证明了它们的算法性能。
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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