Machine Learning-Powered Detection of FASTag Scams: A Proactive Fraud Identification Approach

The security and effectiveness of automated toll collection systems are seriously threatened by FASTag fraud, which can result in monetary losses and inefficient operations. A machine learning-based method for identifying and reducing fraudulent activity in FASTag transactions is suggested by this study. To find irregularities suggestive of fraud, the system makes use of transaction data analysis, user activity monitoring and historical pattern recognition. The model successfully differentiates between authentic and fraudulent transactions in real time by utilizing strategies including anomaly detection, supervised learning and clustering. Through reducing fraud risks, maintaining financial integrity and boosting user confidence in automated toll payment systems, the suggested approach improves the security and dependability of the FASTag architecture. Results from experiments show how well the system detects fraudulent activity, which helps to make the infrastructure for toll collection more secure.