Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 47 , 01 December 2023
Open
Access | Article
Credit Card Fraud Prediction Based on Machine Learning Algorithms
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Abstract
The escalating use of the Internet has led to a surge in online shopping and e-commerce, resulting in a corresponding increase in credit card fraud incidents. Therefore, this research focuses on employing machine learning techniques, which offer enhanced precision and efficiency compared to manual detection, to identify fraudulent activities. To establish the association between credit card transaction attributes and the presence of fraudsters, this study initially gathers data from Kaggle, subsequently normalizing the collected data. Furthermore, the data exhibits severe imbalance, leading to overfitting concerns. To ascertain feature correlations, a correlation heatmap is constructed. Moreover, this investigation selects three models for analysis. Finally, the performance of each model is evaluated using a confusion matrix and derived metrics. The findings reveal that both the decision tree and random forest models exhibit optimal performance, achieving 100% across all indicators. The most influential factors in determining credit card fraud involve the ratio to median purchase price and the geographical proximity of the transaction location to the cardholder's residence.
Keywords
machine learning, credit card fraud prediction, business analysis
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-141-4
- ISBN (Online)
- 978-1-83558-142-1
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/47/20230366
- Copyright
- 01 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited