Car insurance fraud is a high-risk area in insurance and it accounts for as much as 80% of all insurance fraud, with a significant portion of it stemming from the risk of parts leakage. Current anti-leakage techniques in auto insurance mainly rely on the analysis of individual parts data such as vehicle accident records and parts damage lists, which neglects the consideration of the correlation among parts and leads to difficulty in identifying leaked parts effectively. This study proposes a method based on the co-occurrence relationship and density clustering of auto parts to detect parts leakage. In this research, an undisclosed dataset on car insurance fraud was utilized to conduct experiments, and the detection results of parts leakage were obtained. This method takes into account the correlation among auto parts, and possesses higher accuracy and practicality.