Credit Card Fraud Detection using Machine Learning: Methods, Challenges and Solutions

Credit card fraud detection remains a critical challenge in the financial industry, demanding robust, scalable, and adaptive solutions. This paper explores the existing research landscape, identifying significant gaps and tasks such as data imbalance, real-time processing requirements, scalability, and model explainability. Current methodologies, including hybrid approaches like the fusion of "Dempster-Shafer theory" and "Bayesian learning", demonstrate potential but face limitations in adapting to evolving fraud tactics. Advances in machine learning and deep learning, especially through resampling techniques like SMOTE and ensemble methods such as AdaBoost, show promise in improving detection accuracy. It was pointed out that the goal of developing a more comprehensive and efficient fraud detection system could be attained through the integration of various sources of data, including behavioral data and social network data. Toward interdisciplinary collaboration and further innovation, this paper argues for the design of scalable, interpretable, adaptive systems for fraud detection that can operate in real-time. The findings from the responses to these research gaps and challenges will help give results to the defense of the financial industry, especially in cases of credit card fraud, hence ensuring more security with digital transactions.