1. Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017, October). Credit card fraud detection using machine learning techniques: A comparative analysis. In 2017 international conference on computing networking and informatics (ICCNI) (pp. 1-9). IEEE. [
DOI:10.1109/ICCNI.2017.8123782]
2. Bahnsen, A. C., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134-142. [
DOI:10.1016/j.eswa.2015.12.030]
3. Barker, K. J., D'Amato, J., & Sheridon, P. (2008). Credit card fraud: awareness and prevention. Journal of Financial Crime, 15(4), 398-410. doi:10.1108/13590790810907236 [
DOI:10.1108/13590790810907236]
4. Chaudhary, K., Yadav, J., & Mallick, B. (2012). A review of fraud detection techniques: Credit card. International Journal of Computer Applications, 45(1), 39-44.
5. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. [
DOI:10.1145/2939672.2939785]
6. Clarke, Bertrand, Ernest Fokoue, and Hao Helen Zhang. (2009). Principles and Theory for Data Mining and Machine Learning. Springer New York, NY.
https://doi.org/10.1007/978-0-387-98135-2 [
DOI:https://doi.org/10.1007/978-0-387-98135-2.]
7. Dai, S. (2022). Research on Detecting Credit Card Fraud Through Machine Learning Methods. In 2022 2nd International Conference on Business Administration and Data Science (BADS 2022) (pp. 1030-1037). Atlantis Press. [
DOI:10.2991/978-94-6463-102-9_107]
8. Elkan, C. (2001). The foundations of cost-sensitive learning. In International joint conference on artificial intelligence (Vol. 17, No. 1, pp. 973-978). Lawrence Erlbaum Associates Ltd.
9. European Central Bank (2021). Seventh report on card fraud. [online]. Available at: https://www.ecb.europa.eu/pub/cardfraud/html/ecb.cardfraudreport202110~cac4c418e8.en.html [Accessed 6 Jan. 2023].
10. Fawcett, T. (2006a). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861-74. [
DOI:10.1016/j.patrec.2005.10.010]
11. Fawcett, T. (2006b). ROC graphs with instance-varying costs. Pattern Recognition Letters, 27(8), 882-891. [
DOI:10.1016/j.patrec.2005.10.012]
12. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of statistics, 1189-1232. [
DOI:10.1214/aos/1013203451]
13. Greene, W. H. (2000). Econometric analysis [5th edition]. International edition, New Jersey: Prentice Hall.
14. Greene, W. H., & Hensher, D. A. (2010). Modeling ordered choices: A primer. Cambridge University Press. [
DOI:10.1017/CBO9780511845062]
15. Hossain, M. N., Hassan, M. M., & Monir, R. J. (2022). Analyzing the Classification Accuracy of Deep Learning and Machine Learning for Credit Card Fraud Detection. Asian Journal for Convergence in Technology (AJCT) ISSN-2350-1146, 8(3), 31-36. [
DOI:10.33130/AJCT.2022v08i03.006]
16. Legal Dictionary (2023). Fraud - Definition, Meaning, Types, Examples of fraudulent activity. [online] Available at: https://legaldictionary.net/fraud/ [Accessed 6 Jan. 2023]
17. Li, J., & Fine, J. P. (2010). Weighted area under the receiver operating characteristic curve and its application to gene selection. Journal of the Royal Statistical Society: Series C (Applied Statistics), 59(4), 673-692. [
DOI:10.1111/j.1467-9876.2010.00713.x] [
PMID] [
]
18. Microsoft Corporation (2023). LightGBM. Retrived from: https://lightgbm.readthedocs.io/en/latest/ (at 1 Jan 2023).
19. Mojab, R., Heidari, H., & Ebrahimi, S. (2022). Design and Determination of Customer Ranking Model for the Export Development Bank. Tehran: Monetary and Banking Research Institute.
20. Qi, R. (2020). Real-world Credit Card Fraud Detection with Rich Features and Advanced Classification Methods [MSc Dissertation]. School of Computer Science and Informatics. Cardiff University.
21. Shao, J. (1993). Linear model selection by cross‑validation. Journal of the American Statistical Association, 88(422), 486-494. [
DOI:10.1080/01621459.1993.10476408]
22. Shao, J. (1997). An asymptotic theory for linear model selection. Statistica Sinica, 7(2), 221-242.
23. Shaparak (2022). Shaparak Economic Report. Tehran: Shaparak Electronic Payment Network Company. Online access: https://shaparak.ir/, Access date: 01/01/2023.
24. Shen, A., Tong, R., & Deng, Y. (2007, June). Application of classification models on credit card fraud detection. In 2007 International conference on service systems and service management (pp. 1-4). IEEE. [
DOI:10.1109/ICSSSM.2007.4280163]
25. Shi, Y., Li, J., & Li, Z. (2018). Gradient boosting with piece-wise linear regression trees. arXiv preprint arXiv:1802.05640. [
DOI:10.24963/ijcai.2019/476]
26. Stone, M. (1976). An asymptotic equivalence of choice of model by cross‑validation and Akaike's criterion. Journal of the Royal Statistical Society: Series B (Methodological), 38(2), 276-278. [
DOI:10.1111/j.2517-6161.1976.tb00932.x]
27. Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2019, March). Credit card fraud detection-machine learning methods. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-5). IEEE. [
DOI:10.1109/INFOTEH.2019.8717766]
28. Vesta (2023). https://www.kaggle.com/c/ieee-fraud-detection/discussion/101203#589276 (Access date: 1/1/2023).
29. VISA (2023) Payment Security in Multiple Layers (online). Accessed at: 1/9/2023 (https://usa.visa.com/content/dam/VCOM/Media%20Kits/PDF/PaymentSecurity_Infographic.pdf)
30. Zhang, X., Han, Y., Xu, W., & Wang, Q. (2021). HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture. Information Sciences, 557, 302-316. [
DOI:10.1016/j.ins.2019.05.023]