Singh, S.; Nimje, H.; Fulpatile, A.; Neware, R. Credit Card Fraud Detection Using a Hybrid Machine Learning Algorithm. Preprints2024, 2024021206. https://doi.org/10.20944/preprints202402.1206.v1
APA Style
Singh, S., Nimje, H., Fulpatile, A., & Neware, R. (2024). Credit Card Fraud Detection Using a Hybrid Machine Learning Algorithm. Preprints. https://doi.org/10.20944/preprints202402.1206.v1
Chicago/Turabian Style
Singh, S., Ajinkya Fulpatile and Rahul Neware. 2024 "Credit Card Fraud Detection Using a Hybrid Machine Learning Algorithm" Preprints. https://doi.org/10.20944/preprints202402.1206.v1
Abstract
The use of credit cards and online banking is expanding exponentially. As more individuals use debit cards, credit cards, and internet banking, the likelihood of falling victim to various forms of fraud also rises. Due to ignorance, credit card company customers have provided their card details, personal information, and one-time password to an unknown bogus caller on several occasions in the recent past. One of the biggest issues facing financial institutions and customers alike is credit card theft. It may result in large financial losses and harm to the financial institution's image. While there are several ways to identify credit card fraud, one of the best is machine learning.By using past data, machine learning algorithms may be trained to recognize trends and abnormalities that point to fraud. The state-of-the-art in machine learning for credit card fraud detection is reviewed in this study. Numerous research on the same subject have been conducted in the past with a variety of well-known machine-learning techniques. However, with a grasp of the notion of hybrid machine learning, we will now examine how well hybrid machine learning performs on the same problem statement.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
