Deep Learning Fraud Detection in Mobile Payment Transactions

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.

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Publication Details

Journal
Iconic Research and Engineering Journals
Published
2026-04-27
DOI
https://doi.org/10.64388/irev9i10-1716911
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
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article

Deep Learning Fraud Detection in Mobile Payment Transactions

Naveen Kamble, Rohith V, Praful S
Iconic Research and Engineering Journals
Imbalanced Data Classification Techniques
article

Deep Learning Fraud Detection in Mobile Payment Transactions

Naveen Kamble, Rohith V, Praful S
article en

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.

Iconic Research and Engineering JournalsVol. 9(10)
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Imbalanced Data Classification Techniques
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