Events in recent years have led to a fresh wave of discussions about racial justice and equality in the United States. This has led to an increased focus in the insurance industry and regulatory community on bias and equity. However, a lack of consistent data collection is often a significant barrier to the study of disproportionate impacts and equity across race/ethnicity cohorts in various contexts.

In this presentation, we describe a range of techniques for developing probabilistic estimates or predictions of individual race and/or ethnicity. We will show how to apply some of these methods to a simulated dataset to illustrate how to use them in practice. In addition, we will share results from a case study that assesses the predictive performance of these probabilistic estimates using an actual dataset from the insurance industry that has self-reported race/ethnicity recorded.