Detecting Fraudulent Online Transactions Using Deep Neural Networks with SMOTE Oversampling and Explainable AI Techniques

• Author(s): Dr. S Sreeja; C Pavithra
• Paper ID: 1717494
• Page: 969-974
• Published Date: 11-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

Online banking fraud has emerged as a critical challenge in the digital financial ecosystem. With millions of transactions processed daily, traditional rule-based detection mechanisms are insufficient to identify evolving fraudulent patterns. This study applies machine learning and deep learning techniques — specifically an Artificial Neural Network (ANN) — to classify transactions from a publicly available Kaggle dataset as fraudulent or legitimate. The research pipeline encompasses exploratory data analysis, SMOTE-based class imbalance handling, feature engineering, MinMax normalization, and ANN model training with early stopping. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix visualization. To address transparency limitations of black-box models, Explainable AI (XAI) techniques — SHAP and LIME — are incorporated, identifying balance differences, transaction amounts, and transaction type as the key fraud indicators. The findings confirm that ML-based approaches substantially outperform traditional rule-based systems, and that explainability tools are essential for building stakeholder trust in financial AI systems.

Keywords

Online Banking Fraud, Machine Learning, Deep Learning, ANN, SMOTE, SHAP, LIME, Explainable AI, Fraud Detection, Feature Engineering, Classification Model, Data Imbalance

Citations

IRE Journals:
Dr. S Sreeja, C Pavithra "Detecting Fraudulent Online Transactions Using Deep Neural Networks with SMOTE Oversampling and Explainable AI Techniques" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 969-974 https://doi.org/10.64388/IREV9I11-1717494

IEEE:
Dr. S Sreeja, C Pavithra "Detecting Fraudulent Online Transactions Using Deep Neural Networks with SMOTE Oversampling and Explainable AI Techniques" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717494