Anomaly Detection in Toll Transactions Using AI
Pankaj Lembhe
ABSTRACT
While the challenges of generating responses in anomaly detection systems are evident, it is crucial to consider the broader implications of such technology in toll transaction management. For instance, utilizing advanced algorithms to analyze transaction patterns can significantly enhance the identification of fraudulent activities, ensuring that revenue loss is minimized. By employing machine learning techniques, systems can learn from historical data, improving their accuracy over time and adapting to new patterns of behavior that may indicate anomalies [1]. Furthermore, the integration of real-time monitoring capabilities could provide instant alerts, allowing for prompt action to be taken when suspicious transactions are detected, thereby reinforcing the security of toll systems and enhancing user trust.
