Enhancing Financial Fraud Detection Using Neural Networks, Ensemble Models, and Stacking Techniques

Authors

Cibaca Khandelwal

Abstract

Financial fraud detection is a persistent challenge in the banking and e-commerce industries. This study presents a conceptual framework for enhancing fraud detection by integrating neural networks (Multi-Layer Perceptron), ensemble methods (Random Forest, XGBoost, LightGBM), and stacking techniques. By addressing challenges such as class imbalance through Synthetic Minority Oversampling Technique (SMOTE) and reducing dimensionality with Principal Component Analysis (PCA), the proposed framework improves precision, recall, and AUC-ROC metrics. The findings demonstrate that stacking ensembles outperform individual models, providing a robust solution for detecting fraudulent transactions.

Keywords

Financial fraud detection, neural networks, ensemble learning, stacking ensemble, Random Forest, XGBoost, LightGBM, SMOTE, PCA

Enhancing Financial Fraud Detection Using Neural Networks, Ensemble Models, and Stacking Techniques. Cibaca Khandelwal. 2022. IJIRCT, Volume 8, Issue 3. Pages 1-6. https://www.ijirct.org/viewPaper.php?paperId=2502035