Description: This interactive session will compare industry predictions versus what happened with the unwinding of continuous coverage requirements in Medicaid that were first put in place due to the COVID-19 public health emergency. The audience will be able to participate in a guessing game as the panelists review how enrollment could be stabilizing.

Description: Modern Actuarial work, being quite data intensive, provides many opportunities for actuaries to use the statistical skills that are part of their basic training. Furthermore, modern computing allows us to analyze vast quantities of data intensively, and each package will provide statistical 'answers' to any questions the actuary might ask of it. However, in this particular field, it is often worth taking a step back, and ensuring that we recall and consider the warnings that come with each of the statistical procedures we might attempt to use. This will allow us to ensure that we guard ourselves against drawing inappropriate conclusions, which could come back to haunt us later.At the conclusion of the session, attendees will be able to avoid drawing inappropriate statistical conclusions, while doing their work. They will leave informed about the types of pitfalls that exist, and where those might come into play in actuarial work.

Description: Learn how modernization of data, models, and technology are influencing workflows. This webcast will address how workflows are changing in response to new regulatory requirements and the impacts the changes have on modeling and end-to-end future state operating models. Industry leading actuaries will discuss: How team collaboration can increase quality and speed of reporting to internal and external stakeholders How to strengthen controls and compliance frameworks across finance and actuarial departments How to modernize actuarial input/output for strategic analysis and insights How to attract and retain top talent by improving opportunities for actuaries There will also be dedicated time to ask questions of those who have made the transition.

Description: Artificial intelligence (AI) models have become increasingly popular in various industries, including finance. Such models can help lenders assess the risk of loan applicants, identify potential defaults and make better decisions about granting credit. However, the complexity of many machine-learning and, particularly, deep-learning models makes it difficult to understand how they arrive at their decisions. This lack of interpretability can be a significant problem in the legal and ethical realms, as it hinders transparency and accountability. Join us to learn interpretability techniques that are essential to address the challenges of using complex AI models in credit risk modeling. These techniques aim to provide a better understanding of how an AI model arrived at a particular decision or prediction. Techniques include surrogate models, which aim to provide a simpler model that approximates the behavior of the original model, and decision trees, which show the decision-making process in a more transparent way. However, applying interpretability techniques to AI models can be a challenging task. For example, some techniques can reduce the performance of the model, which can be unacceptable in applications where high accuracy is critical. Additionally, some techniques may only be effective for certain types of models or datasets, which limits their applicability. Despite these challenges, interpretability techniques are becoming increasingly important in the field of credit risk modeling. Financial regulators have recognized the importance of interpretability, and are starting to require financial institutions to use models that can be explained and validated. In addition, interpretability techniques can be used to improve the performance of credit risk models. By understanding how a model makes decisions, it is possible to identify areas where the model can be improved or where additional data may be needed.

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: Emerging Applications of ESG Methods offers ground-up insights on cutting edge applications of economic scenario generation. The course will focus on the incorporation of climate change risk in scenario modeling and investment applications of ESGs. The following topics will be covered: Approaches to climate risk modeling. Climate risk is very different than classical financial economic risk. Radical uncertainty is amenable to scenario analysis and lack of data. Building special deterministic scenarios that go beyond NGFS requirements as well as bespoke scenarios. Holistic Approach to Climate Risk Modeling. Overview of different Climate Risk Modeling Applications. Use of ESG scenarios for Strategic Asset Allocation (SAA). Liquidity risk management.

Description: The document is a Request for Data Contributions for the 2022-23 Variable Annuity Contract Holder Behavior Experience Study. It outlines the purpose of the study, data submission procedures, important dates, data transmission instructions, study outputs, benefits to data contributors, and data access and ownership details.

Description: CMS has required hospitals to publish their chargemaster rates, cash discounts and negotiated rates as of January 1, 2021. Our panel is comprised of a data strategist, an executive from a large hospital system and an actuary.

Description: In this session, we will delve into the application of survey data analytics to extract valuable insights from public survey datasets. The discussion will start with the basics of survey design, sampling practices, and estimation of key statistics. Three case studies will be presented: one in the realm of population health and another in medical economics, both demonstrating the use of either SAS or R to perform the survey analytics.

Description: Data is at the core of the actuaries' role within companies, but how can actuaries translate data insights into informed and actionable decision making? The key is communicating data insights into a language that transcends analysis and enables real-time decisions that can provide an advantage to your management team. Learn an approach to choosing a tool or medium for your data insights, questions to ask yourself to better understand the communication necessary, and perspectives on how actuaries can take a larger role in actionable decisions within the industry. Understand availability, quality, and practical application of data sources health actuaries can access easily, how that data looks, approaches to transforming data into decisions, how it enriches administrative and claims data, and applications to pricing and reserving. Finally, discuss implications of ASOP 23, ASOP 41, and ASOP 56 as it relates to this topic. By attending this session, you will be able to: - Create an action plan on how to transform data into messaging for your leaders. - Use real experiences to formulate a strategy to expand the actuaries' role in informing decisions within the industry, including examples, to illustrate how data can benefit financials of health coverages. - Understand the application of ASOP 23 (Data), ASOP 41 (Communication), and ASOP 56 (Modeling) as it relates to turning data into decisions. - Be knowledgeable about new sources of morbidity for medical care and LTC and how to acquire and apply this information in actuarial models.