Description: This webcast will walk through several statistical methods for imputing race and ethnicity from U.S. datasets, highlighting their strengths and weaknesses, talking about how to use them and evaluating their accuracy, and telling you where you can get code packages as well as a free dataset to try the methods out.

Description: A non-technical review of two recent general insurance articles published in the SOA's North American Actuarial Journal. The articles reviewed analyze Pay-As-You-Drive Insurance and Usage-Based Insurance.

Description: The CAS has sponsored research which explores potential exposure bases for rating cyber liability. This short article gives some highlights from that research.

Description: Dive into an overview of potential customer value propositions, trends in consumer acceptance and adoption, solving the data portability issue, potential impacts of emerging partner ships with OEMs and more within the auto insurance industry during our latest webcast. Experience interactive dialogue that will allow another dimension of thinking as industry experts provide insights and expertise. Throughout this webcast, you can expect developments in the personal lines automotive telematics-based insurance landscape that will develop a further understanding of the present and future of this industry.

Description: To evaluate the risk associated with personal auto coverage, insurers have traditionally relied on information about the driver (such as age, gender and marital status), location (such as territory or ZIP code), the intended use of the vehicle (such as business or pleasure) and information about the vehicle when new (such as original cost). Insurers then added detail to driver information using sophisticated credit scoring and demographic models, and added detail to vehicle use information with telematics and usage-based insurance pricing. Now, insurers are adding detail about the vehicle itself using vehicle history. The presenters will discuss how carriers use vehicle history to more accurately price and underwrite risks. They will discuss obvious data elements, such as salvage title brands, verified average annual mileage and severe damage, as well as data that may not be as obvious but has proven predictive of loss in the past. Finally, using vehicle history as an entry point for UBI programs will be covered.

Description: Article discussing how geospatial imagery and high-resolution image technology are transforming property insurance.

Description: This session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.

Description: There is value in a framework that allows a smooth transition from a qualitative assessment to a quantitative analysis as entities mature in their understanding of climate risk. In this report, a case study on climate change impact on home prices is used to illustrate a Bayesian network iterative approach to building a model for scenario analysis.

Description: The Casualty Actuarial Society, the Society of Actuaries Research Institute, the Society of Actuaries Joint Risk Management, Reinsurance and Financial Reporting Sections are pleased to make available key findings from the 2022 Emerging Risks Survey, the sixteenth in the series. Conducted by Max J. Rudolph of Rudolph Financial Consulting LLC, the survey incorporated a set of Emerging Risks defined by the World Economic Forum as the basis for several of the questions. The survey also included questions related to current risk management topics.