Fastag Fraud Detection System

Sakshi Dodawad, Sachin Somappa Sidnal, Shraddha G Shahapurkar, Shreya Kadakol, Shweta Madiwalar, Neelam Somannavar

2025

Abstract

The increasing adoption of FASTag for electronic toll collection has streamlined vehicular payments across toll plazas in India but has also introduced new risks of fraud. This paper presents a machine learning-based approach for detecting fraudulent transactions in FASTag systems, implemented in Python. By analyzing transaction patterns, identifying anomalies, and employing classification and anomaly detection algorithms, our proposed system detects potential fraud in real time. This solution aims to reinforce the security and integrity of the FASTag ecosystem, safeguarding against unauthorized usage and financial loss. Our study includes a review of existing fraud detection methods in digital payment systems, followed by an evaluation of our approach through performance metrics such as accuracy and precision. Experimental results demonstrate the system’s effectiveness in identifying suspicious activities, thus providing a valuable tool for enhancing security in electronic tolling infrastructure.

Paper Citation

in Harvard Style

Dodawad S., Sidnal S., Shahapurkar S., Kadakol S., Madiwalar S. and Somannavar N. (2025). Fastag Fraud Detection System. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 904-909. DOI: 10.5220/0013607000004664

in Bibtex Style

@conference{incoft25,
author={Sakshi Dodawad and Sachin Sidnal and Shraddha Shahapurkar and Shreya Kadakol and Shweta Madiwalar and Neelam Somannavar},
title={Fastag Fraud Detection System},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={904-909},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013607000004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Fastag Fraud Detection System
SN - 978-989-758-763-4
AU - Dodawad S.
AU - Sidnal S.
AU - Shahapurkar S.
AU - Kadakol S.
AU - Madiwalar S.
AU - Somannavar N.
PY - 2025
SP - 904
EP - 909
DO - 10.5220/0013607000004664
PB - SciTePress