Skip to main content

ORIGINAL RESEARCH article

Front. Comput. Sci.
Sec. Computer Security
Volume 6 - 2024 | doi: 10.3389/fcomp.2024.1402339

Fostering Fraud Detection: Leveraging Deep Neural Network-Based Classifier in Online Banking Security

Provisionally accepted
Priti Saxena Priti Saxena 1*R.B. Patel R.B. Patel 2
  • 1 Research Scholar, Computer Science Dept. , VMSB Uttarakhand Technical University, Uttarakhand Technical University, Dehradun, India
  • 2 Computer Science Dept., Chandigarh College of Engineering and Technology, Chandigarh, India

The final, formatted version of the article will be published soon.

    The rise of electronic payment methods in corporate transactions has resulted in a surge in fraudulent activity, presenting substantial hurdles for financial institutions. Effective fraud detection methods are crucial in this situation. This research suggests a new method for detecting fraud by combining oversampling technique with a deep neural network classifier. Our solution seeks to tackle the inherent imbalances and complexities in credit card transaction data without using ensemble learning techniques. The deep neural network classifier is trained on a balanced dataset to detect patterns that suggest fraudulent behavior. The proposed strategy is assessed using an extensive dataset and compared to current methodologies. The experimental results confirm the effectiveness of our method in identifying fraudulent transactions, highlighting its potential as a reliable solution for fraud detection in electronic payment systems.

    Keywords: anomaly detection, Credit card, Classification, online banking, Fraud detection

    Received: 17 Mar 2024; Accepted: 23 May 2024.

    Copyright: © 2024 Saxena and Patel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Priti Saxena, Research Scholar, Computer Science Dept. , VMSB Uttarakhand Technical University, Uttarakhand Technical University, Dehradun, India

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.