Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach

作     者：Gupta, Rahul Kumar Hassan, Asmaul Majhi, Samir Kumar Parveen, Nikhat Zamani, Abu Taha Anitha, Raju Ojha, Binayak Singh, Abhinav Kumar Muduli, Debendra 

作者机构：C V Raman Global Univ Dept Comp Sci & Engn Bhubaneswar Odisha India Western Governors Univ Dept Informat Technol 4001 S 700 300 Millcreek UT 84107 USA Univ Bisha Coll Comp & Informat Technol Dept Artificial Intelligence Al Khuzama Saudi Arabia Northern Border Univ Fac Sci Dept Comp Sci Ar Ar 73213 Saudi Arabia Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 522302 AP India 

出 版 物：《RESULTS IN ENGINEERING》 (Result. Eng.)

年 卷 期：2025年第26卷

核心收录：

基　　金：Deanship of Scientific Research at Northern Border University  Arar  KSA [NBU-FFR-2025-1850-03] 

主　　题：Credit card fraud detection SMOTE-ENN Autoencoder TOPSIS Ensemble learning Particle swarm optimization Stacking model 

摘      要：Credit card fraud is an emerging global issue that causes substantial financial losses and undermines consumer trust in digital transactions. With the increase in online payment volumes, conventional fraud detection technologies are increasingly confronted by the complexity of fraudulent strategies that require intelligent and scalable alternatives. This study introduces an innovative machine learning-based fraud detection framework that incorporates sophisticated preprocessing methods like SMOTE-ENN for class imbalance mitigation, autoencoder for dimensionality reduction, and TOPSIS for optimal feature selection. A stacking ensemble model is developed with support vector machine (SVM), K-nearest neighbors (KNN), and extreme learning machine (ELM) to enhance forecast accuracy. The particle swarm optimization (PSO) algorithm is employed to optimize ELM parameters, enhancing generalization and model convergence. Extensive tests with standard datasets show outstanding results, achieving 99.95% accuracy, 99.93% precision, and 99.97% recall in detecting fraud. The outcomes highlight the model s proficiency in properly identifying fraudulent transactions while reducing false positives. The proposed method provides a viable alternative for secure and efficient credit card fraud detection in contemporary digital economy, characterized by high accuracy and real-time scalability.