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A Hybrid Approach for Fraud Detection in Digital Wallet Transactions Using Adversarial Autoencoders and Gated Recurrent Units

Authors

  • Shaik Janbhasha Department of Computer Science and Engineering (Data Science), CVR College of Engineering, Telangana, India
  • C. H. N. Santhosh Kumar CSE Department, ANURAG Engineering College, Kodad, India
  • Vedula Sitharamulu Department of Computer Science and Engineering, GITAM School of Technology, Telangana, India
  • BNV Madhu Babu CSE Department, Teegala Krishna Reddy Engineering College, Telangana, India
  • Hanumantha Rao Battu Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, India
  • K. Venkataramana CSE (DS) Department, Malla Reddy Engineering College, Telangana, India
Volume: 15 | Issue: 4 | Pages: 25532-25537 | January 1970 | https://doi.org/10.48084/etasr.10898

Received: 10 March 2025 | Revised: 9 April 2025 and 24 April 2025 | Accepted: 1 May 2025 | Online: 


Corresponding author: Vedula Sitharamulu

Abstract

Digital payment systems are increasingly used to complete everyday transactions; however, their digital nature exposes users to the risk of fraudulent activity, necessitating advanced detection techniques to ensure security. This study proposes a hybrid fraud detection model for digital wallet transactions by integrating Adversarial Autoencoders (AAE) and Gated Recurrent Units (GRU), combining AAE's ability to learns robust latent representations, and GRU's ability to capture temporal dependencies within transaction sequences. The proposed method outperforms existing approaches, achieving 99% accuracy, 99% recall, 98% F1-score, 99% precision, and 99.4% Area Under the Curve (AUC). By effectively reducing both false positives and false negatives, the model improves fraud detection and mitigates financial risks in digital transactions.

Keywords:

deep learning, fraud detection, unsupervised learning

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References

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How to Cite

[1]
Janbhasha, S., Kumar, C.H.N.S., Sitharamulu, V., Madhu Babu, B.N.V., Battu, H.R. and Venkataramana, K. A Hybrid Approach for Fraud Detection in Digital Wallet Transactions Using Adversarial Autoencoders and Gated Recurrent Units. Engineering, Technology & Applied Science Research. 15, 4, 25532–25537. DOI:https://doi.org/10.48084/etasr.10898.

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Copyright (c) 2025 Shaik Janbhasha, C. H. N. Santhosh Kumar, Vedula Sitharamulu, BNV Madhu Babu, Hanumantha Rao Battu, K. Venkataramana

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