Enhancing credit card fraud detection with a stacking-based hybrid machine learning approach.

ABSTRACT: The swift progression of technology has increased the complexity of cyber fraud, posing an escalating challenge for the banking sector to reliably and efficiently identify fraudulent credit card transactions. Conventional detection approaches fail to adapt to the advancing strategies of fraudsters, resulting in heightened false positives and inefficiency within fraud detection systems. This study overcomes these restrictions by creating an innovative stacking hybrid machine learning (ML) approach that combines decision trees (DT), random forests (RF), support vector machines (SVM), XGBoost, CatBoost, and logistic regression (LR) within a stacking ensemble framework. This method uses stacking to integrate diverse ML models, enhancing predictive performance, with a meta-model consolidating b

SUBMITTER: Marazqah Btoush EAL 

PROVIDER: S-EPMC12453863 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature