Automating Risk Assessment: The Role of Artificial Intelligence in Insurance Underwriting

Abstract

Automating risk assessment through artificial intelligence (AI) can significantly transform the insurance underwriting process by improving accuracy and efficiency. This paper explores the development and implementation of a hybrid machine learning model that integrates logistic regression and support vector machines (SVM) for enhanced underwriting risk assessment. By meticulously analyzing historical claim data and detailed customer profiles, this hybrid model achieves a notable accuracy of 91%, far surpassing the 81.3% accuracy typically associated with manual underwriting practices. Logistic regression is utilized for its simplicity and effectiveness in modeling relationships between dependent and independent variables. It helps in identifying key risk factors from the dataset, providing clear insights into how various customer attributes influence claim likelihood. Support vector machines (SVM) are then applied to classify and predict the likelihood of claims, leveraging their strength in handling both linear and non-linear data. The combination of these two methods results in a robust predictive model capable of delivering highly accurate risk assessments. The model's ability to predict claims with such high accuracy not only enhances the precision of risk assessments but also significantly speeds up the underwriting process. This reduction in processing time can lead to faster policy issuance, improved customer satisfaction, and operational efficiencies within insurance companies. Furthermore, the AI-driven approach enables insurers to identify high-risk individuals more accurately, allowing for better resource allocation and risk management.