Improvement of Bank Fraud Detection Through Synthetic Data Generation with Gaussian Noise DOI Creative Commons
Fray L. Becerra-Suarez,

Halyn Alvarez-Vasquez,

Manuel G. Forero

и другие.

Technologies, Год журнала: 2025, Номер 13(4), С. 141 - 141

Опубликована: Апрель 4, 2025

Bank fraud detection faces critical challenges in imbalanced datasets, where fraudulent transactions are rare, severely impairing model generalization. This study proposes a Gaussian noise-based augmentation method to address class imbalance, contrasting it with SMOTE and ADASYN. By injecting controlled perturbations into the minority class, our approach mitigates overfitting risks inherent interpolation-based techniques. Five classifiers, including XGBoost convolutional neural network (CNN), were evaluated on augmented datasets. achieved superior performance noise-augmented data (accuracy: 0.999507, AUC: 0.999506), outperforming These results underscore noise’s efficacy enhancing accuracy, offering robust alternative conventional oversampling methods. Our findings emphasize pivotal role of strategies optimizing classifier for financial data.

Язык: Английский

A Novel Approach based on XGBoost Classifier and Bayesian Optimization for Credit Card Fraud Detection DOI Creative Commons
Mohammed Tayebi, Said El Kafhali

Cyber Security and Applications, Год журнала: 2025, Номер unknown, С. 100093 - 100093

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Improvement of Bank Fraud Detection Through Synthetic Data Generation with Gaussian Noise DOI Creative Commons
Fray L. Becerra-Suarez,

Halyn Alvarez-Vasquez,

Manuel G. Forero

и другие.

Technologies, Год журнала: 2025, Номер 13(4), С. 141 - 141

Опубликована: Апрель 4, 2025

Bank fraud detection faces critical challenges in imbalanced datasets, where fraudulent transactions are rare, severely impairing model generalization. This study proposes a Gaussian noise-based augmentation method to address class imbalance, contrasting it with SMOTE and ADASYN. By injecting controlled perturbations into the minority class, our approach mitigates overfitting risks inherent interpolation-based techniques. Five classifiers, including XGBoost convolutional neural network (CNN), were evaluated on augmented datasets. achieved superior performance noise-augmented data (accuracy: 0.999507, AUC: 0.999506), outperforming These results underscore noise’s efficacy enhancing accuracy, offering robust alternative conventional oversampling methods. Our findings emphasize pivotal role of strategies optimizing classifier for financial data.

Язык: Английский

Процитировано

0