Fraud Detection In Fastag Payments

Abstract

The widespread deployment of FASTag for electronic toll collection across India has significantly improved traffic efficiency and reduced manual intervention. However, this rapid digitization has also introduced new vulnerabilities, including unauthorized transactions, duplicate tag usage, and GPS spoofing attacks. Conventional rule-based fraud detection systems are increasingly ineffective in identifying such evolving and sophisticated fraud patterns. To overcome these limitations, this study proposes a machine learning-driven fraud detection framework that utilizes historical transaction data, behavioral analytics, and anomaly detection techniques to identify fraudulent FASTag activities in real time.The proposed system integrates supervised learning models, particularly Random Forest, for accurate fraud classification, alongside unsupervised methods such as Isolation Forest and Autoencoders to detect anomalous transaction patterns. Key attributes considered in the analysis include transaction timestamp, vehicle classification, toll plaza location, and user behavior trends. Furthermore, the framework incorporates real-time data processing through streaming analytics to enable immediate detection and response to suspicious activities.Experimental evaluation indicates that the proposed approach significantly enhances detection accuracy while reducing false positives compared to traditional rule-based methods. By continuously adapting to emerging fraud patterns, the system ensures scalability, robustness, and improved financial security. This research contributes to the development of a secure, intelligent, and efficient digital toll collection ecosystem, making it highly suitable for large-scale deployment in modern transportation infrastructures.