Description: The insurance industry is faced with many unforeseen changes such as recent advances in technologies and the associated impacts on the risk assessments. In this session, we focus on two specific developments; (i) the liquidity provision problem on automated market makers (a.k.a. DeFi exchanges such as Uniswap) and the conditions under which liquidity providers should enter or exit the DeFi token pools; and (ii) efficient utilization of drivers telematics data for better risk classification in personal auto insurance.

Description: Building new models or migrating from one system to another is hard! During this session, you will hear from your colleagues who have gone through such projects. You will gain a better sense of what to expect, what to avoid, and what to prioritize for such projects. The discussion will be system agnostic. Learn about pitfalls and how to avoid them!

Description: Watch as presenters highlight the SOA's work on an updated Medicaid underwriting margin model. This research project includes the model along with a set of instructions and a paper describing the key components of underwriting margin, including cost of capital and risk margin. The model was created as a tool for Medicaid actuaries representing both MCO and state principals to determine appropriate underwriting margin components within Medicaid managed care rates. In addition, explore a brief description of the motivation for an underlying theory behind the model. Walk through different model inputs and run through some realistic scenarios that Medicaid actuaries may encounter in states where they operate.

Description: Private LTC insurers are paving the way for value-based care through working to implement care management programs that improve the lives of their policyholders before and during a claim. However, measuring the effectiveness of these new programs is a challenging task. In this article, we will explore powerful tools that can enhance traditional effectiveness studies and address some of their existing weaknesses.

Description: It goes without saying that the analysis of data is a key aspect of actuarial practice, but do traditional actuarial approaches allow us to see the whole picture? Effective data analysis is also key to the identification of health disparities and measuring progress on health equity. Actuaries and others analyzing data for health equity purposes find themselves looking at data differently.

Description: This session will discuss essential theories and practical applications of deep learning for actuaries. Participants will gain insights on leveraging these cutting-edge techniques to enhance actuarial models and drive predictive accuracy.

Description: Tree models have been found to provide a good fit to group long term disability recovery and mortality rates. This research paper provides an overview of tree models, and details how they have been used for developing these rates.

Description: Regression to the mean (RTTM) is inflating your health care intervention evaluations if you are not carefully managing it's omnipresent and significant impact RTTM is the statistical phenomenon which arises when comparing sequential data points when an initial observation is extreme or an outlier, and states that with no other intervention, future observations will be closer to the average. Members who had high costs last year will, on average, have lower costs this year, even in the absence of any intervention or clinical program, simply by 'regressing' to the mean. This means that if your clinical programs are simply targeting members who had adverse medical experience last year, you’re targeting the wrong people. And if your savings methodologies are simply last year’s costs minus this year’s costs, the savings are inflated. By attending this session, you will be able to identify situations where RTTM will be a significant factor, how to eliminate it's impact when performing retrospective evaluations, how to identify prospects for clinical intervention with RTTM in mind, and more.

Description: The article provides a high-level, non-technical, introduction to artificial neural networks (ANNs) and deep learning with an application to risk management. The method relies on typical ANN components such as nodes, activation functions, and optimization through gradient descent.

Description: Actuarial Standards of Practice (ASOP) #12 on Risk Classification is being revised while the Academy of Actuaries Issue Brief on Discrimination, published in August 2023, is shaping how bias and discrimination are thought through as actuaries. Individual states like Colorado and New York are suggesting approaches, and the Society of Actuaries, the Casualty Actuarial Society and other actuarial associations are providing the actuaries the tools to address practical cases.