Credit card fraud detection and classification by deep learning and machine learning

Kiran Bala *, Sakshi sharma, Meenakshi Garg and Deeksha Verma
Department of computer science and Engineering in Chandigarh Engineering College, Jhanjeri, Mohali, India. 
 
Review Article
Global Journal of Engineering and Technology Advances, 2022, 13(03), 022-027.
Article DOI: 10.30574/gjeta.2022.13.3.0202
Publication history: 
Received on 02 November 2022; revised on 12 December 2022; accepted on 15 December 2022
 
Abstract: 
One of the most important contributors to the expansion and progression of a nation's economy is its banking and financial industry. In particular, over the recent past, there has been a significant increase in the utilization of credit and debit cards, whereby all customers trade transactions either digitally over the internet or physically at the stores. Here, the customers, banking institutions, and financial organizations are all being put in a difficult position by fraudulent actors. Because more recent technology is now readily available, internet banking has become an important avenue for commercial transactions. Fake banking activities and fraudulent transactions are serious problem that affects both the users' sense of safety and their trust in the system. In addition, fraudulent activities result in enormous losses because of the proliferation of sophisticated frauds such as virus infections, scams, and fake websites. These frauds are all examples of advanced fraud. This study makes three contributions toward the prevention of fraudulent activity involving credit card transactions.
 
Keywords: 
Machine learning; AI; Credit card; Deep learning
 
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