Advanced Machine Learning Approaches for Credit Card Fraud Detection in the USA: A Comprehensive Analysis - Sizan, Mir Mohtasam Hossain; Chouksey, Anchala; Tannier, Nikhil Rao; Jobaer, Md Abdullah Al; Akter, Jasmin; Roy, Ashutosh; Ridoy, Mehedi Hasan; Sartaz, M Saif; Islam, Dewan Aminul (Journal of Ecohumanism, 2025)
Credit card fraud is a financial threat in America, both for financial institutions and for consumers, and it is growing in severity. Traditional fraud detection methods become less effective in countering emerging fraud trends, and for that reason, sophisticated algorithms in machine learning have to be embraced. This research project strived to develop and compare complex algorithms for fraud detection in credit cards in America. With a variety of algorithms including both unsupervised and supervised learning, this study strived towards improving fraud transaction detection rates. This study focuses on real-world credit card transaction datasets from America, offering a robust foundation for comprehending the intricacies of fraud detection in an authentic financial context. Employing actual transaction data, the study aims to replicate and model variation and nuance in fraud and consumer behavior, such that any developed machine learning algorithms will have a basis in real-life realities. For model selection, we deployed several machine learning models, notably Logistic Regression, Random Forest, and XG-Boost Classifier. In evaluating model performance, several key metrics, including Precision, Recall, and the F1-score, were taken into consideration. Random Forest Classifier performed best overall, with relatively high accuracy for fraud prediction, and average recall, with a marginally high level of F1-score. Overall, it can be noticed that Random Forest has the most balanced performance out of the three in fraud detection capabilities, which seems to be a necessity. The integration of real-time fraud prevention with machine learning models is revolutionizing financial institution transaction monitoring. ML models can analyze and process information in real-time, and thus, allow for effective and efficient real-time fraud monitoring. The future of fraud detection holds many exciting avenues for research, most prominently in deep model development. Methods in deep learning, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), have been successful in discovering complex structures and sequential relations in transactional information. Another promising avenue for future research is combining AI-powered identity verification with blockchain technology for fraud prevention.