Credit Card Fraud Detection Using Machine Learning and Risk-Based Alert Strategies
TL;DR
In data environments with severe class imbalances, a machine learning model can be designed as an effective triage tool and its actual value is not only to predict fraud, but also to establish a low-risk release, medium-risk verification, and high-risk manual review of the decision-making process for institutions and truly reduce losses.
Abstract
Credit card fraud has become a central issue for the financial security of banks and cardholders. However, fraudulent transactions account for only a very small proportion of all transaction cases. If it is missed, it may directly cause huge property damage. As a result, this study focuses on how to identify fraudulent transactions as much as possible, while controlling false positives and human audit costs. Specifically, logistic regression, gradient boosting, XGBoost, and random forest are compared, while class weighting, SMOTE, and Random Undersampling are evaluated for handling class imbalance. It is more critical to translate the model results into a risk warning system and a loss simulation system. The results show that random forest with class weighting has the best overall performance, with 90.6% F1-Score, 94.1% precision, and 87.3% recall. Meanwhile, under the cost assumptions, this model reduces simulation costs by 85.4% relative to the no-model baseline. These findings suggest that in data environments with severe class imbalances, a machine learning model can be designed as an effective triage tool. Its actual value is not only to predict fraud, but also to establish a low-risk release, medium-risk verification, and high-risk manual review of the decision-making process for institutions and truly reduce losses.