A Review of Credit Card Fraud Detection using Machine Learning
Prof. Sumit Sharma
, Deepak Gwale
Credit card fraud , machine learning, dataset preprocessing, feature selection, algorithm selection
Credit card fraud has become a significant concern in the financial industry, resulting in substantial financial losses for both financial institutions and cardholders. To mitigate this issue, machine learning algorithms have been widely applied in credit card fraud detection systems. This paper provides a comprehensive review of the existing literature on credit card fraud detection using machine learning techniques. The aim is to analyze and evaluate the effectiveness of different machine learning algorithms and methodologies employed in this domain. The review covers various aspects such as dataset preprocessing, feature selection, algorithm selection, and performance evaluation metrics. Additionally, emerging trends and challenges in credit card fraud detection are discussed, providing insights for future research directions.
"A Review of Credit Card Fraud Detection using Machine Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 7, page no.584 - 589, July-2023, Available :https://ijsdr.org/papers/IJSDR2307083.pdf
Volume 8
Issue 7,
July-2023
Pages : 584 - 589
Paper Reg. ID: IJSDR_207777
Published Paper Id: IJSDR2307083
Downloads: 000347290
Research Area: Engineering
Country: -, -, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
