Description: In the insurance industry, machine learning models are increasingly used to make decisions that affect people's lives. In order to ensure that models are fair, they should be audited for bias. There are no hard and fast rules for bias audit; metrics that can be used to evaluate fairness vary depending on the context in which the model is deployed. In this session, we present a flexible framework that highlights key considerations in bias evaluation that can be adjusted to suit your process. In this session, we will learn about different metrics to assess fairness, discuss open source tools that can be used for bias audit, and walk through a case study.

Description: Predictive modeling is a powerful toolset for assumption setting (mortality, lapse, etc.). The multivariate approach has its advantages in handling high-dimensional data, address correlations, and statistical significance. However, building insurance assumptions is far more than a simple modeling practice. One must take care to address how the model is applied and its business implications. This session will share the learnings from multiple projects.