Life insurance provides trillions of dollars of financial security for hundreds of millions of individuals and families worldwide. Life insurance companies must accurately assess individual-level mortality risk to simultaneously maintain financial strength and price their products competitively. The traditional underwriting process used to assess this risk is based on manually examining an applicant’s health, behavioral, and financial profile. The existence of large historical data sets provides an unprecedented opportunity for artificial intelligence and machine learning to transform underwriting in the life insurance industry. In this talk, I will present an overview of how we combined one of the largest application data sets in the industry with a responsible artificial intelligence framework to develop a mortality model and life score. I will describe how the life score serves as the primary risk-driving engine of deployed algorithmic underwriting systems and demonstrate its high level of accuracy, yielding a nine-percent reduction in deaths within the healthiest pool of applicants.