AI-Based Motor Insurance Risk Assessment

Abstract

The rapid evolution of intelligent transportation systems has created a growing demand for data-driven motor insurance models that evaluate risk more accurately. Traditional methods rely heavily on static factors such as driver profiles, vehicle categories, and historical claims, which fail to represent real-time driving behavior. This paper presents an AI-Based Motor Insurance Risk Assessment System leveraging gyroscope and accelerometer data to analyze driver motion and predict accident likelihood. The system captures orientation and movement changes from onboard sensors to identify harsh acceleration, sudden braking, and sharp turns—critical indicators of unsafe driving. Machine learning algorithms process this data to classify drivers based on risk level and support fair, data-driven premium estimation. Experimental results demonstrate improved precision in identifying highrisk drivers and reducing manual assessment errors. The proposed framework provides a scalable, intelligent approach for building safer and more transparent insurance ecosystems.