Journal of Intelligent Learning Systems and Applications

Volume 11, Issue 3 (August 2019)

ISSN Print: 2150-8402   ISSN Online: 2150-8410

Google-based Impact Factor: 4.31  Citations  h5-index & Ranking

Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms

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DOI: 10.4236/jilsa.2019.113003    707 Downloads   2,351 Views   Citations


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.

Cite this paper

Gao, J. , Zhou, Z. , Ai, J. , Xia, B. and Coggeshall, S. (2019) Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms. Journal of Intelligent Learning Systems and Applications, 11, 33-63. doi: 10.4236/jilsa.2019.113003.

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