Summary
This document is a technical report detailing Nayya's approach to benefits recommendations. It outlines the four main pillars that the recommendation engine evaluates, which include health factors from previous healthcare utilization, current conditions, and existing prescriptions. Additionally, it considers total wealth and spending ability through optional inputs such as household income and existing assets. The report explains how Nayya assesses potential additional benefits needs based on upcoming procedures and life events, as well as individual employee preferences regarding provider affinity and risk tolerance. The methodology emphasizes the importance of data-driven recommendations to prevent underutilization or overspending on benefits. Nayya's unique approach leverages machine learning and a 'human-in-the-loop' model, where trained actuaries provide feedback on the recommendation outputs, ensuring continuous improvement of the algorithm. This results in personalized recommendations that prioritize employee health and financial protection.
