Actuarial Experience Studies and Assumption Setting in R by Matt Heaphy Visit https://rstats.ai/nyr/ to learn more. Abstract: Actuarial modeling of insurance liabilities requires long-term assumptions for mortality, policyholder behavior, and a variety of other variables. Traditional actuarial assumption setting relies on performing experience studies and developing tabular rate tables considering a limited number of features. Modern data science tools like R can allow actuaries and data scientists to develop more robust and granular assumptions considering a wider variety of features. These techniques can have profound impacts on valuation and product management. Bio: Matt Heaphy is a Vice President and Actuary at Nassau Financial Group. He leads the Data Analytics team which provides a broad range of services including data warehousing, enterprise‐wide reporting, experience studies, and cross‐functional analytics support. Matt has held a variety of actuarial leadership roles leading life and annuity pricing, annuity product management, annuity valuation, and hedging. Matt is passionate about programming, data visualization, predictive modeling, and all things R. Matt holds a Bachelor of Science Degree from the University of Connecticut, is a Fellow of the Society of Actuaries, and is a Member of the American Academy of Actuaries. Twitter: https://twitter.com/entreaphy Presented at the 2022 New York R Conference (June 9, 2022)