Abstract
Nowadays, people are using credit cards more frequently, especially for online shopping. Credit card fraud has a negative influence on individuals, merchants, and financial institutions. This paper focuses on credit card fraud detection by using three supervised machine learning methods: decision tree, random forest, and AdaBoost algorithm. These three methods are widely used in the medical field, chemical field, visual identification, and finance field. We use the confusion matrix and receiver operating characteristic curve to interpret our model. The random forest has the highest accuracy of 99.6% in predicting non-fraud. The AdaBoost has the highest accuracy of 77% in predicting fraud transitions, while it has the lowest accuracy of 95% in predicting non-fraud. Therefore, the random forest algorithm is the best model to apply in credit card fraud detection.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
