Lightweight Neural Network based UPI Fraud Detection for Transactional Security

This document introduces a lightweight fraud detection system powered by a neural network, specifically developed to safeguard real-time transactions within the Unified Payments Interface (UPI). As digital payment systems become increasingly popular, the threat of fraud has grown, highlighting the need for smart and effective detection solutions. The proposed system employs a neural network model that has been trained on transactional and behavioral characteristics to accurately pinpoint suspicious activities. Essential components of the system encompass data annotation, environment configuration, neural prediction, and real-time visualization using a web interface. It is designed to function in low-latency settings while providing efficient fraud detection and maintaining computational resource efficiency. The findings underscore its appropriateness for scalable, practical implementation in contemporary digital payment frameworks.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-07
DOI
https://doi.org/10.5281/zenodo.15355937
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Lightweight Neural Network based UPI Fraud Detection for Transactional Security

Zenodo (CERN European Organization for Nuclear Research)
Imbalanced Data Classification Techniques
article

Lightweight Neural Network based UPI Fraud Detection for Transactional Security

article en

Abstract

This document introduces a lightweight fraud detection system powered by a neural network, specifically developed to safeguard real-time transactions within the Unified Payments Interface (UPI). As digital payment systems become increasingly popular, the threat of fraud has grown, highlighting the need for smart and effective detection solutions. The proposed system employs a neural network model that has been trained on transactional and behavioral characteristics to accurately pinpoint suspicious activities. Essential components of the system encompass data annotation, environment configuration, neural prediction, and real-time visualization using a web interface. It is designed to function in low-latency settings while providing efficient fraud detection and maintaining computational resource efficiency. The findings underscore its appropriateness for scalable, practical implementation in contemporary digital payment frameworks.

Zenodo (CERN European Organization for Nuclear Research)
Decent work and economic growth
Openalex Percentile: Top 77%
Imbalanced Data Classification Techniques
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