Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms
Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms作者机构：Hebei University of Economics and Business Shijiazhuang China China University of Political Science and Law Beijing China Wuhan Maple Leaf International School (High School) Wuhan China Wuhan Jinde Education Consulting Co. Ltd. Wuhan China University of Southern California Los Angeles USA
出 版 物：《Journal of Intelligent Learning Systems and Applications》 (智能学习系统与应用（英文）)
年 卷 期：2019年第11卷第3期
页 面：33-63页
学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Credit Card Fraud Machine Learning Algorithms Logistic Regression Neural Networks Random Forest Boosted Tree Support Vector Machines
摘 要：Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data. The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results. Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines. The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set. The resulting model can be utilized in a credit card fraud detection system. A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity.
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