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Deep Learning Fraud Detection in Mobile Payment Transactions
Subject area: Science,Engineering and Technology · Area of research: Fintech
DOI: https://doi.org/10.64388/IREV9I10-1716911
Abstract
With growing digital UPI payment platforms for online payment, concurrently there is rise in digital fraudulent activities which can cause financial loss for individuals, businesses, Govt entities. So to prevent such losses and digital fraudulent activities there is requirement of Deep learning models. Deep learning is nothing but a branch of Machine learning which uses neural network to learn large set of previously stored data and analyze real-time transactions to conclude the transactions as genuine or fraud and detect the suspicious activity. It inspects thoroughly advances in models, such as Artificial Neural Network [ANN], Recurrent Neural Network [RNN], Long-short term memory [LSTM], Gated recurrent units [GRU], and Auto-encoders. As a key volunteer, this study initiates Deep Learning-Sector-Governance [DLSG] framework. By incorporating above introduced models, this review offers practical counselling for researchers, industry practitioners working to avert fraud activities.
Keywords
Deep learning; Machine learning; ANN; RNN; LSTM; GRU; Auto-encoders; Fraud detection, UPI.
References
[1] Chen, Y., Zhao, C., Xu, Y., Nie, C., Zhang, Y., Deep Learning in Financial Fraud Detection: Innovations, Challenges, and Applications, Data Science and Management, https:// doi.org/10.1016/j.dsm.2025.08.002.
[2] Ms. Nibedita Mukhopadhyay and Prof. Dr. Mahadeb Mukhopadhyay (2024). UPI FRAUDS A STUDY ON UPI USAGE, AWARENESS AND IMPACT IN INDIA, International Journal of Research in Commerce and Management Studies (IJRCMS) 6 (6): 179-197 Article No. 312 Sub Id 594.
[3] Ahmed, M., Mahmood, A. N., & Islam, M. R. (2016). A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 55, 278–288.
[4] Hajek P, Abedin MZ, Sivarajah U. Fraud Detection in Mobile Payment Systems using an XGBoost-based framework. Inf Syst Front. 2022 Oct 14: 1 – 19. Doi: 10. 1007 /s10796-022-10346-6. Epub ahead of print. PMID: 36258679; PMCID: PMC9560719.
[5] Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93, 106384.
How to cite this paper
@article{1716911,
author = {Naveen Kamble, Praful S, Rohith V},
title = {Deep Learning Fraud Detection in Mobile Payment Transactions},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {3208-3211},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716911.pdf},
abstract = {With growing digital UPI payment platforms for online payment, concurrently there is rise in digital fraudulent activities which can cause financial loss for individuals, businesses, Govt entities. So to prevent such losses and digital fraudulent activities there is requirement of Deep learning models. Deep learning is nothing but a branch of Machine learning which uses neural network to learn large set of previously stored data and analyze real-time transactions to conclude the transactions as genuine or fraud and detect the suspicious activity. It inspects thoroughly advances in models, such as Artificial Neural Network [ANN], Recurrent Neural Network [RNN], Long-short term memory [LSTM], Gated recurrent units [GRU], and Auto-encoders. As a key volunteer, this study initiates Deep Learning-Sector-Governance [DLSG] framework. By incorporating above introduced models, this review offers practical counselling for researchers, industry practitioners working to avert fraud activities.},
keywords = {Deep learning; Machine learning; ANN; RNN; LSTM; GRU; Auto-encoders; Fraud detection, UPI.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716911}
}