Using Accelerated Supervised Machine Learning Algorithms (ASMLA) as a Tool in Life Insurance Underwriting

This chapter applies Accelerated Supervised Machine Learning Algorithms (ASMLA), a method employed by various researchers, to enhance underwriting efficiency. We implement different ASMLA models combined with optimized preprocessing techniques to accelerate and improve risk assessment in life insurance underwriting. Accelerated underwriting relies on both traditional and non-traditional, non-medical data used within predictive models or machine learning algorithms to perform some of the tasks of an underwriter. This chapter investigates the application of Accelerated Supervised Machine Learning Algorithms (ASMLA) for risk classification in life insurance underwriting. Utilizing a synthetic dataset of 100,000 applicants, the study successfully categorizes individuals into four distinct risk tiers. The results indicate that the models achieve not only a high degree of predictive accuracy but also maintain explainability, underscoring the potential of ASMLA to render the underwriting process both more efficient and equitable.