Compliant Transport Vehicles Verification Fraud Detection of Based on Rule Inference

Abstract

This phenomenon can be described as an intentional act of lying on the compliant vehicle verification with the intent to obtain an illegal operation certificate of transport. These false data will bring safety problems in the management of transport vehicles. Regrettably, the fraud behaviors of compliant vehicle verification are too hidden to come to light, therefore access to labeled historical information is extremely limited. For this reason, the applicability of supervised machine learning techniques for compliant vehicle verification fraud detection is severely hindered. Such limitations motivate the contribution of this work. We present a novel approach for the detection of potential fraudulent compliant transport vehicle verification using only rule inference techniques and allowing the future use of supervised learning techniques. We demonstrate the ability of our model to identify potential fraudulent verification vehicles on compliant transport vehicle verification data, reducing the number of potential fraudulent verification vehicles. The obtained results demonstrate that our model doesn’t miss on real compliant transport vehicles verification data, increasing the operational efficiency in the compliant transport vehicles verification process without needing historic labeled data.