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.