2026 , Volume 31, № 2, p.120-135

Due to increasing complexity and frequency of credit card fraud, there is a critical need for highly accurate and efficient detection systems. This study proposes an enhanced fraud detection framework that combines ensemble learning with feature importance techniques to improve performance. It uses six powerful boosting algorithms — AdaBoost, XGBoost, GBM, LightGBM, CatBoost, and LogitBoost — as base models, which are then merged using a stacked ensemble method to boost prediction accuracy. To ensure model efficiency and interpretability, feature selection techniques such as recursive feature elimination, tree-based importance, mutual information classification, and ANOVA F-test are applied, with the ANOVA method prioritized in the final model. When evaluated on a benchmark dataset, the proposed system achieved exceptional results: accuracy, precision, recall, and F-measure of 99.97 %. This demonstrates the effectiveness of stacked ensembles in combining the strengths of individual models while minimizing errors. The feature selection process also improves computational efficiency by focusing on the most relevant features.