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Detecting Fraudulent Online Transactions Using Deep Neural Networks with SMOTE Oversampling and Explainable AI Techniques
Subject area: Science,Engineering and Technology · Area of research: Deep Learning and Explainable AI
DOI: https://doi.org/10.64388/IREV9I11-1717494
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
References
[1] Ali, A. et al. (2022). Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review. Applied Sciences, 12(19), 9637.
[2] Alvarado Zabala, J., Martillo Alchundia, I., & Guzman Seraquive, G. (2022). Literature Review on Machine Learning Techniques in Bank Fraud Detection. Sapienza International Journal of Interdisciplinary Studies, 3(1), 719–727.
[3] George, M. Z. H., Alam, M. K., & Hasan, M. T. (2023). Machine Learning for Fraud Detection in Digital Banking: A Systematic Literature Review. ASRC Procedia: Global Perspectives in Science and Scholarship, 3(1), 37–61.
[4] Husnaningtyas, N. & Dewayanto, T. (2023). Financial Fraud Detection and Machine Learning Algorithm (Unsupervised Learning): Systematic Literature Review. Jurnal Riset Akuntansi dan Bisnis Airlangga, 8(2).
[5] Vanini, P., Rossi, S., Zvizdić, E., & Domenig, T. (2023). Online Payment Fraud: From Anomaly Detection to Risk Management. Financial Innovation, 9(66).
[6] Koppireddy, C. S., & Devi, R. V. D. S. V. (2025). Fraud Detection in Banking: A Deep Learning Approach with Explainable AI. Journal of Soft Computing Paradigm, 7(3), 258–275.
[7] Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., & Bontempi, G. (2018). Scarff: A Scalable Framework for Streaming Credit Card Fraud Detection with Spark. Information Fusion, 41, 182–194.
[8] Bahnsen, A. C., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature Engineering Strategies for Credit Card Fraud Detection using Decision Trees. Expert Systems with Applications, 51, 134–142.
[9] Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.
[10] Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774.
[11] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
[12] Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357.
How to cite this paper
@article{1717494,
author = {Dr. S Sreeja, C Pavithra},
title = {Detecting Fraudulent Online Transactions Using Deep Neural Networks with SMOTE Oversampling and Explainable AI Techniques},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {969-974},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717494.pdf},
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},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717494}
}