Rich Moncher recently co-authored a research paper with Jessica Leong and Kate Jordan, and it was published by the Casualty Actuarial Society. Each paper in this research series addresses a different aspect of race and insurance pricing as viewed through the lens of property and casualty insurance, and this paper in particular provides an overview of emerging regulations on bias and fairness in the insurance industry, as well as other industries.
As the insurance industry increases its use of models, machine learning, and artificial intelligence, regulatory scrutiny has intensified, particularly with regard to concerns about unfair discrimination. In the past, regulation centered around model inputs, specifying certain variables that could not be used. Now, new regulations are emerging around testing model outcomes for unfair discrimination, with requirements to report the findings. This paper provides actuaries with information and tools to proactively consider fairness in their modeling process and navigate this new regulatory landscape. Click to view the paper, as well as other papers in the series.
Data quality, credibility, and variability are critical factors in assessing potential unfair discrimination. Actuaries should analyze data sets for early indicators of unfair discrimination and consider data improvement strategies. They can also keep in mind fairness considerations as they transform data, such as how they infer missing values, as well as develop and cap losses. If you are in need of an independent property and casualty actuary to analyze your data sets for early indicators of unfair discrimination and consider data improvement strategies, we invite you to schedule a call with us today.
We also welcome your feedback by posting a comment or contacting us at support@SIGMAactuary.com.
© SIGMA Actuarial Consulting Group, Inc.