Open Access Subscription Access
An Approach to Detect Credit Card Fraud Utilizing Machine Learning
With the increasing popularity of Credit card usage, Credit Card fraud also increases. The number of online payment options has expanded thanks to e-commerce and several other websites, raising the possibility of online fraud. As a result, both people and financial institutions suffer significant losses. This research seeks to detect credit card fraud and make attempts to cut down on it. Financial institutions place a high priority on identifying and stopping fraudulent activity. Fraud prevention and detection are pricey, time-consuming, and labor-intensive processes. Several machinelearning algorithms can be utilized for detection. In order to evaluate past customer transaction information and identify behavioral traits, the study's main goal is to develop and apply a special fraud detection algorithm for simulcasting transaction data. Through the research, try to give a genuine solution to Credit card users and make their transactions secure. This research aims to propose a trustworthy and efficient way for identifying credit card fraud. The accuracy of several autonomous classifiers using machine learning that were employed for recognition is compared and examined. The Random Forest classifier has the highest accuracy of 99.98%.
Credit card fraud, Neural network, Random Forest, Imbalanced classification, Machine learning.
- . P. D. C. Soulé-Dupuy, "Credit Card Fraud Detection using," International Journal of Computer Science and Mobile Computing, no. 2022, 2019.
- . S. G. Vaishnavi Nath Dornadulaa, "ScienceDirect," Elsevier B.V, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/p ii/S187705092030065X. [Accessed 22 October 2022].
- . R. R. S. S. D. A. A. P. Nayan Uchhana, "Literature Review of Different Machine Learning Algorithms for Credit Card Fraud Detection," International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 10, no. 6, April 2021, p. 8, 2021.
- . M. K. G. Sonal Mehndiratta, "Credit Card Fraud Detection Techniques," International Journal of Computer Science and Mobile Computing, vol. 8, no. 8, p. 7, 2019.
- . ai Kiran, J. G., “ Credit card fraud detection using KNN,Naive Bayes model-based classifier. International Journal of Advance Research, Ideas, and Innovations in Technology, 44-46.
- . Chaudhary, R. R. (ISSN: 2278-3075 (Online), Volume-10 Issue-6). A Survey on Credit Card. International Journal of Innovative Technology and Exploring Engineering (IJITEE).
- . Han, S. (25-26 June 2010,2011). Credit Card Fraud Detection. 2009 International Joint Conference on Artificial Intelligence.
- . Darshan kaur(student), c. l. (n.d.). Machine Learning Approach for Credit Card Fraud Detection (KNN & Naïve. 1st international conference on intelligent communication and computational research (ICICCR-2020).
- . V Kumar K S, V.K.V.G., V Shankar A, Pratibha K, Credit card fraud detection using machine learning Algorithms. International Journal of Engineering Research & Technology, 2020
- . Xuan, S., et al. Random Forest for credit card fraud detection. in 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). 2018. IEEE
- . Decision Tree Based Algorithm for Intrusion Detection [Journal] / auth. Kajal Rai M.Syamala Devi,Ajay Guleria. - [s.l.] : Dec 28,2015. - 04 Pages:28282-2834(2016) : Vol. 07.
- . SARA MAKKI, Z. A.-S. ( publication July 8, 2019, current version July 29, 2019.). An Experimental Study With Imbalanced Classification Approaches for Credit Card Fraud Detection. Special Section on Advanced Software and Data Engineering for Secure Societies, 93010.
Abstract Views: 20
PDF Views: 0