TY - JOUR
AU1 - Faraji, Zahra
AU2 - 
AB - SEISENSE Journal of Management Vol 5 No 1 (2022): DOI: https://doi.org/10.33215/sjom.v5i1.770 , 49-59 Research Article A Review of Machine Learning Applications for Credit Card Fraud Detection with A Case study 1 * Zahra Faraji Financial Service Analytics Institution, University of Delaware, United States * Corresponding author: zfaraji@udel.edu Purpose - This paper aims to highlight the widely used supervised Article History techniques applied for fraud detection. In addition, this paper aims Received 2022-01-14 to apply some techniques to evaluate their performance on real- Revised 2022-02-10 world data and develop an ensemble model as a potential solution Accepted 2022-02-03 for this problem. Published 2022-02-15 Design/Methodology- Different techniques applied in this study Keywords for fraud detection purposes are logistic regression, decision tree, Machine learning, random forest, KNN, and XGBoost. The confusion matrix gives Credit card fraud detection, information about the assignment of inputs to the different classes. Artificial Neural Networks, This study uses precision and recall to evaluate the performance, Logistic regression, calculated based on the confusion matrix. Random Forests, Findings- XGBoost is the fastest and is expected to have the best XGBoost performance; however, it is only outperforming the random forest How to cite? in terms of accuracy, precision, recall, and 
TI - A Review of Machine Learning Applications for Credit Card Fraud Detection with A Case study
JF - SEISENSE Journal of Management
DO - 10.33215/sjom.v5i1.770
DA - 2022-02-15
UR - https://www.deepdyve.com/lp/unpaywall/a-review-of-machine-learning-applications-for-credit-card-fraud-oTrQpMmdr3
DP - DeepDyve
ER -