Efficient Credit Card Fraud Detection System Using Big Data and Machine Learning
DOI:
https://doi.org/10.32628/IJSRSET2411426Keywords:
Credit Card Fraud Detection, Big Data, Machine Learning, Anomaly Detection, Real-Time Monitoring, Data SecurityAbstract
This review offers a detailed strategy to address the growing threat of credit card fraud in today's digital landscape. By utilizing Big Data analytics alongside machine learning methods, the system aims to transform fraud detection processes. It tackles the challenges arising from the increasing volume and complexity of credit card transactions, enabling the real-time detection and prevention of fraudulent actions. The system employs sophisticated machine learning algorithms to identify patterns and anomalies linked to fraudulent activities, allowing for proactive responses to emerging fraud tactics. Additionally, the system is optimized to handle and analyze large datasets efficiently, ensuring timely and precise detection of fraud. It also incorporates strong security protocols to protect sensitive customer data while adhering to privacy regulations. This review ultimately seeks to enhance the safety and reliability of electronic payments, protecting financial institutions and consumers from the harmful effects of credit card fraud.
Downloads
References
Fanai, H., & Abbasimehr, H. (2023). A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection. Expert Systems With Applications, 217,119562. https://doi.org/10.1016/j.eswa.2023.119562
Cherif, A., Badhib, A., Ammar, H., Alshehri, S., Kalkatawi, M., & Imine, A. (2023). Credit card fraud detection in the era of disruptive technologies: A systematic review. Journal of King Saud University - Computer and Information Sciences, 35(1), 145–174. https://doi.org/10.1016/j.jksuci.2022.11.008
Carcillo, F., Pozzolo, A. D., Borgne, Y. L., Caelen, O., Mazzer, Y., & Bontempi, G. (2018). SCARFF: A scalable framework for streaming credit card fraud detection with spark. Information Fusion, 41, 182–194. https://doi.org/10.1016/j.inffus.2017.09.005
Gupta, P., Varshney, A., Khan, M. R., Ahmed, R., Shuaib, M., & Alam, S. (2023). Unbalanced Credit Card Fraud Detection Data: A Machine Learning-Oriented Comparative Study of Balancing Techniques. Procedia Computer Science, 218, 2575–2584. https://doi.org/10.1016/j.procs.2023.01.231
Madhuri, T., Babu, E. R., Uma, B., & Lakshmi, B. M. (2021). Big-data driven approaches in materials science for real-time detection and prevention of fraud. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.04.323
Vaughan, G. (2020). Efficient big data model selection with applications to fraud detection. International Journal of Forecasting, 36(3), 1116–1127. https://doi.org/10.1016/j.ijforecast.2018.03.002
Maniraj, S. P., Saini, A., Ahmed, S., & Sarkar, S. D. (2019). Credit Card Fraud Detection using Machine Learning and Data Science. International Journal of Engineering Research & Technology (IJERT), 8(9), 1–8. https://doi.org/10.17577/IJERTV8IS090031
Saheed, Y. K., Baba, U. A., & Raji, M. A. (2022). Big Data Analytics for Credit Card Fraud Detection Using Supervised Machine Learning Models. In Big Data Analytics in the Insurance Market (pp. 1-15). ISBN: 978-1-80262-638-4, eISBN: 978-1-80262-637-7. https://doi.org/10.1108/978-1-80262-637-720221019
Zareapoor, M., Seeja, K. R., & Alam, M. A. (2012). Analysis on credit card fraud detection techniques: Based on certain design criteria. International Journal of Computer Applications, 52(3), 35–42. https://doi.org/10.5120/8184-1538
Alenzi, H. Z., & Aljehane, N. O. (2020). Fraud detection in credit cards using logistic regression. International Journal of Advanced Computer Science and Applications, 11(12). https://doi.org/10.14569/ijacsa.2020.0111265
Sailusha, R., Gnaneswar, V., Ramesh, R., & Rao, R. R. Credit card fraud detection using machine learning. Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS 2020).
Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques: A comparative analysis. 2017 International Conference on Computing Networking and Informatics (ICCNI). https://doi.org/10.1109/iccni.2017.8123782
Tanouz, D., Subramanian, R. R., Eswar, D., Reddy, G. V., Kumar, A. R., & Praneeth, C. H. V. (2021). Credit card fraud detection using machine learning. 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). https://doi.org/10.1109/iciccs51141.2021.9432308
Kiran, S., Guru, J., Kumar, R., Kumar, N., Katariya, D., & Sharma, M. (2018). Credit card fraud detection using Naïve Bayes model based and KNN classifier. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3).
Saheed, Y. K., Hambali, M. A., Arowolo, M. O., & Olasupo, Y. A. (2020). Application of GA feature selection on Naive Bayes, random forest and SVM for credit card fraud detection. 2020 International Conference on Decision Aid Sciences and Application (DASA). https://doi.org/10.1109/dasa51403.2020.9317228
Daly, L. (2021, October 27). Identity theft and credit card fraud statistics for 2021: The Ascent. The Motley Fool. Retrieved from https://www.fool.com/theascent/research/identity- theft-credit-card-fraud-statistics/
Maes, S., Tuyls, K., Vanschoenwinkel, B., & Manderick, B. (2002). Credit card fraud detection using Bayesian and neural networks. Proceedings of the 1st International Naiso Congress on Neuro Fuzzy Technologies, 261-270.
Wasokun GB, Omomule TG, Akinyede RO.Encryption and tokenization-based system for credit card information security. Int J Cyber Sec Digital Forensics. 2018;7(3):283–93.
Burkov, A. (2019). The Hundred-Page Machine Learning Book (pp. 3–5).
Dornadula VN, Geetha S.Credit card fraud detection using machine learning algorithms. Proc Comput Sci. 2019;165:631–41. https://doi.org/10.1016/j.procs.2020.01.057
Lebichot, B., Borgne, Y.-A. L., He-Guelton, L., Oblé, F., & Bontempi, G. (2019). Deep-learning domain adaptation techniques for credit card fraud detection. *In INNS Big Data and Deep Learning Conference* (pp. 78-88). Springer. https://doi.org/10.1007/978-3-030-11799-6_10
Raghavan, P., & El Gayar, N. (2019). Fraud detection using machine learning and deep learning. *2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)* (pp. 334-339). https://doi.org/10.1109/ICCIKE.2019.8920882
Dal Pozzolo, A., Caelen, O., Le Borgne, Y.-A., Waterschoot, S., & Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. *Expert Systems with Applications, 41*(10), 4915-4928. https://doi.org/10.1016/j.eswa.2014.02.011
Pillai, T. R., Hashem, I. A. T., Brohi, S. N., Kaur, S., & Marjani, M. (2018). Credit card fraud detection using deep learning technique. *2018 Fourth International Conference on Advances in Computing Communication & Automation (ICACCA)* https://doi.org/10.1109/ICACCA.2018.8377038
Kazemi, Z., & Zarrabi, H. (2017). Using deep networks for fraud detection in credit card transactions. *2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI)* (pp. 0630-0633). https://doi.org/10.1109/KBEI.2017.8311471
Shenvi, P., Samant, N., Kumar, S., & Kulkarni, V. (2019). Credit card fraud detection using deep learning. *2019 IEEE 5th International Conference for Convergence in Technology (I2CT)* (pp. 1-5). https://doi.org/10.1109/I2CT45612.2019.906568 2
Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. *Information Sciences, 479*, 448-455. https://doi.org/10.1016/j.ins.2018.12.015
Bahnsen, A. C., Aouada, D., Stojanovic, J., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. *Expert Systems with Applications, 51*, 134-142. https://doi.org/10.1016/j.eswa.2016.01.031
Mekterović, I., Karan, M., Pintar, D., & Brkić, L. (2021). Credit card fraud detection in card-not- present transactions: Where to invest? *Applied Sciences, 11*(15), 6766. https://doi.org/10.3390/app11156766
Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Oble, F., & Bontempi, G. (2021). Combining unsupervised and supervised learning in credit card fraud detection. *Information Sciences, 557*, 317-331. https://doi.org/10.1016/j.ins.2020.12.058
Lakshmi, S., & Kavilla, S. D. (2018). Machine learning for credit card fraud detection system. *International Journal of Applied Engineering Research, 13*(24), 16819-16824. https://doi.org/10.37622/IJAER/13.24.2018.16819-16824
A. Alshammari, R. Alshammari, M. Altalak, K. Alshammari and A. Alhakamy, "Credit-card Fraud Detection System using Big Data Analytics," 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Maldives, Maldives, 2022, pp. 1-7, doi: 10.1109/ICECCME55909.2022.9987791.
Pandey, N., Rajeshwari, S., Shobha Rani, B. N., & Mounica, B. (2018). Credit card fraud detection using big data framework. *International Journal of Creative Research Thoughts (IJCRT), 6*(2), 523.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
