Credit card fraud detection using supervised machine learning methods

Hanzeng Wang

doi:10.1117/12.2628121

22 April 2022 Credit card fraud detection using supervised machine learning methods

Hanzeng Wang

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121634F (2022) https://doi.org/10.1117/12.2628121
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

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.

Citation Download Citation

Hanzeng Wang "Credit card fraud detection using supervised machine learning methods", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121634F (22 April 2022); https://doi.org/10.1117/12.2628121

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available