Volume 1 Issue 1 | 2024 | View PDF
Paper Id: IJMSM-V1I4P102
doi: 10.71141/30485037/V1I1P102

Machine Learning for the Identification of Credit Card Fraud

Safrin S, Madhu S

Citation:
Safrin S, Madhu S, "Machine Learning for the Identification of Credit Card Fraud" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 4, pp. 07-14, 2024.

Abstract:
The most prevalent problem in the world right now is identifying credit card theft. This can be explained by the rise in online transactions and e-commerce platforms. The most frequent ways that credit card fraud happens are when a card is lost or stolen and used without permission, or even when the cardholder uses their personal information for illicit purposes. The modern world has several credit card problems. To detect fraudulent behavior, the credit card fraud detection system was developed. The initiative aims to investigate machine learning techniques. In this research, we propose to detect fraudulent transactions by using the Kaggle dataset. We fit the dataset to Random Forest to ascertain whether a transaction is fraudulent or not. Finally, we compared the performance of XGBoost and LightGBM.

Keywords: Fraudulent transactions, Fraud prevention, Feature engineering, Fraud detection systems, Data mining, Fraud patterns, Predictive modeling.

References:
1. Esraa Faisal Malik et al., “Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture,” Mathematics, vol. 10, no. 9, pp. 1-16, 2022.
2. Pooja Tiwar et al., “Credit Card Fraud Detection Using Machine Learning,” 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, pp. 1264-1270, 2021.
3. Harish Paruchuri, “Credit Card Fraud Detection Using Machine Learning: A Systematic Literature Review,” ABC Journal of Advanced Research, vol. 6, no. 2, pp. 1-8, 2017.
4. K.M. Anil Kumar et al., “Detection of False Income Level Claims Using Machine Learning,” International Journal of Modern Education and Computer Science, vol. 14, no. 1, pp. 1-13, 2022.
5. Yvan Lucas, and Johannes Jurgovsky, “Credit Card Fraud Detection Using Machine Learning: A Survey,” Arxiv, pp. 1-31, 2020.
6. Suresh K Shirgave et al., “A Review on Credit Card Fraud Detection Using Machine Learning,” International Journal of Scientific & Technology Research, vol. 10, no. 8, pp. 1-4, 2019.
7. Emmanuel Ileberi, Yanxia Sun, and Zenghui Wang, “A Machine Learning Based Credit Card Fraud Detection Using the GA Algorithm for Feature Selection,” Journal of Big Data, vol. 9, pp. 1-17, 2022.
8. Meera AlEmad, “Credit Card Fraud Detection Using Machine Learning,” Rochester Institute of Technology RIT Scholar Works, pp. 1-34, 2022.
9. Abrar Hayat Nadim et al., “Analysis of Machine Learning Techniques for Credit Card Fraud Detection,” 2019 International Conference on Machine Learning and Data Engineering (iCMLDE), Taipei, Taiwan, pp. 42-47, 2019.
10. Salomi Hurriya Anjum, and Geeta Patil, “Cheat Detection for Credit Cards Using Artificial Intelligence,” 2022 IEEE North Karnataka Subsection Flagship International Conference (NKCon), Vijaypur, India, pp. 1-6, 2022.
11. Mohammed Azhan, Shazli Meraj, “Credit Card Fraud Detection using Machine Learning and Deep Learning Techniques,” 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, pp. 514-518, 2020.
12. Shantanu Rajora et al., “A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance,” 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, pp. 1958-1963, 2018.