June 14, 2023 – AI usage has taken off in the insurance industry, as compute and data storage resources have gotten cheaper over time. Carriers using AI must adhere to basic principles in the solution architecture to ensure nondiscriminatory and fair outcomes. Moreover, carriers must be able to demonstrate the fairness and transparency of their AI processes to various regulators—a challenge, given that many algorithms are designed to be somewhat opaque.
This report discusses how CIOs and CTOs can ensure that the AI programs they use are explainable and transparent, keeping them in line with regulatory requirements. Aite-Novarica Group reviewed its research, conversations with carriers and vendors, and external articles to develop the checklist for explaining AI and ML algorithms to regulators.
Clients of Aite-Novarica Group’s Life, Annuities, & Benefits or Property & Casualty service can download this report and the corresponding charts.
This report mentions Arize, Cloud Object Storage, Dask, DVC, Grafana, Kubeflow, MLflow, Nvidia, PyTorch, Scale, Snowflake, Spark, Spell, Suberb.ai, Tecton, TensorFlow, and UbiOps.
About the Author

Mitch Wein
Mitch Wein is an Executive Principal in the Insurance Practice at Datos Insights. He has expertise in international IT leadership and transformation as well as technology strategy for banking, insurance (life, annuities, personal, commercial, specialty), and wealth management. Prior to joining Datos Insights, Mitch served in senior technology management positions at numerous financial institutions. At Bankers Trust (now Deutsche Bank), he automated...
Other Authors

Jack Krantz
Jack Krantz is a Senior Associate at Datos Insights. He works with senior team members to support consulting projects and create research reports. Prior to joining the firm, he was a firefighter, a postdoctoral investigator at the Woods Hole Oceanographic Institution, and a professor at Brown University. He has both a Ph.D. and an M.Sc. in Earth, Environmental, and Planetary Sciences...