Credit card fraud detection using machine learning: A comprehensive survey and analysis
Shruti Chougule, Kishor Mane · 2025
The increasing reliance on credit cards for online and offline transactions has led to a significant rise in fraud, causing substantial financial losses for consumers and financial institutions. This paper presents a comprehensive survey of machine learning techniques employed for fraud detection of credit card, highlighting their effectiveness in detecting fraud transactions in real-time. Various algorithms, including supervised as well as unsupervised learning methods, are analyzed to evaluate their performance in handling large datasets and adapting to evolving fraud patterns. Key challenges such as data imbalance, model interpretability and feature selection are discussed, emphasizing their impact on the effectiveness of fraud detection systems. By synthesizing existing research and advancements in the field, this paper aims to underscore the potential of machine learning to enhance the accuracy and effectiveness of credit card fraud detection, ultimately contributing to more secure financial transactions.