Vol. 5 No. 2 (2026): April
RESEARCH ARTICLES

Online Banking System with AI-Based Fraud Detection

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  • R M Mallika,
  • Erasappa Murali,
  • G Girish,
  • T Ganesh Naidu,
  • P M Jagan,
  • K Hemanth
R M Mallika
Department of Computer Science and Engineering, Siddharth Institute of Engineering and Technology, Puttur, A.P, India.
Erasappa Murali
Department of Computer Science and Engineering, Siddharth Institute of Engineering and Technology, Puttur, A.P, India.
G Girish
Department of Computer Science and Engineering (Cloud Computing), Siddharth Institute of Engineering and Technology, Puttur, A.P, India.
T Ganesh Naidu
Department of Computer Science and Engineering (Cloud Computing), Siddharth Institute of Engineering and Technology, Puttur, A.P, India.
P M Jagan
Department of Computer Science and Engineering (Cloud Computing), Siddharth Institute of Engineering and Technology, Puttur, A.P, India.
K Hemanth
Department of Computer Science and Engineering (Cloud Computing), Siddharth Institute of Engineering and Technology, Puttur, A.P, India.
DOI: https://doi.org/10.5281/zenodo.18955453

Published 2026-03-11

Keywords

  • AI-driven fraud detection,
  • hybrid model,
  • machine learning,
  • sequence-based analysis,
  • real-time fraud detection,
  • financial security,
  • transaction monitoring
    • ...More
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How to Cite

R M Mallika, Erasappa Murali, G Girish, T Ganesh Naidu, P M Jagan, & K Hemanth. (2026). Online Banking System with AI-Based Fraud Detection. International Journal of Computational Learning & Intelligence, 5(2), 996–1016. https://doi.org/10.5281/zenodo.18955453

Copyright (c) 2026 R M Mallika, Erasappa Murali, G Girish, T Ganesh Naidu, P M Jagan, K Hemanth

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

 

The rapid development of online banking services has significantly reshaped the financial industry, and while it has offered greater convenience. It has also created greater risks of fraud. Conventional anti-fraud systems, which are mostly rule-based, are not very effective in keeping up with the ever-changing nature of fraud attempts. The fraud detection system in the financial sector has been facing a major challenge in dealing with the increasing rate of fraud. In order to address this problem, we have proposed a hybrid model for fraud detection in the context of online banking. In our proposed model, we have used supervised classification, anomaly detection, and sequence-based behavioral analysis for the detection of fraud in the context of online banking. We have used various machine learning algorithms like Random Forest, Isolation Forest, and Long Short-Term Memory (LSTM) for the proposed system. We have achieved an impressive 96% accuracy, with a precision, recall, and F1-score of 95%, thereby outperforming the conventional system. We have demonstrated that our proposed system can be used to improve security as well as user experience in the context of digital banking transactions. We have discussed the limitations of the conventional fraud detection system as well as the potential advancements in the proposed system.

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