A Machine Learning Framework for Enhancing Insurance Claim Settlements through Driver Behavior Analysis

Abstract

The increasing integration of technology in the automotive and insurance industries has led to the development of advanced systems for optimizing insurance claim settlements. This paper proposes a machine learning-based framework designed to enhance the accuracy, efficiency, and transparency of insurance claim management by analyzing driver behavior. By leveraging real-time data collected from vehicle sensors, such as speed, acceleration, braking patterns, and steering angles, the proposed framework utilizes machine learning algorithms, including decision trees, support vector machines, and neural networks, to classify driver behavior, such as cautious, aggressive, or distracted. These classifications are then used to assess the severity of incidents and evaluate the legitimacy of insurance claims. The framework aims to reduce fraudulent claims by providing data-driven evidence of driver behavior at the time of the incident, leading to more accurate claim assessments. Additionally, the continuous monitoring and feedback provided by the system encourage safer driving habits, ultimately fostering a safer driving environment and improving customer satisfaction. The proposed machine learning framework is a significant step towards revolutionizing the insurance industry by streamlining the claim settlement process, reducing operational costs, and promoting responsible driving.