Description: Can you answer the question, 'What are the chances we will lose more than $1 million?' without saying, 'It depends'? If you miss a projection, can you estimate the chances that the miss was due to random variation versus an incorrect projection? Listen in as presenters demonstrate how to answer these questions and others using total risk analysis. Total risk analysis includes quantifying both the random variation risk and the projection risk associated with actuarial functions like pricing and reserving. After attending this session, you will be able to describe total risk analysis and to apply to many health actuarial functions.

Description: This session will showcase how from an actuarial perspective IFRS17 has its roots in market consistent valuations. It will also show how International Financial Reporting Standard (IFRS) 17 is rooted in probability-weighted stochastic scenarios-same as fair valuation, and how initial recognition sets the stage, as in the pricing basis and report, for actual experience to emerge as in the subsequent recognition. A detailed model will be given for a simple annual premium non-par endowment and actual numbers given and explained. The stochastic model will explain in detail the key reserve items in IFRS 17 including: - Liability for remaining coverage (LRC) - Risk Adjustment (RA) - Contractual Service Margin (CSM) And it will explain their interactions with one another. A discussion will be held regarding universal life policies with simple interest guarantees. And how these guarantees merit a valuation similar to Lookback options because of their path-dependent nature.

Description: Session 5A: Creating Continuity in Behavioral Health for Medicaid & ACA Beneficiaries

Description: Runtime of actuarial models and their associated processes has been a growing concern amongst the industry. This session will explore how expanding regulatory requirements, refined analytical objectives, and the desire for process optimization have increased the amount of processing power needed to get answers in a timely manner. Presenters will dive into how companies are leveraging data, refined pre- and post-processing routines, runtime reduction techniques, and cloud-based solutions to increase their efficiency in producing, analyzing, and validating model results.

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: 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: 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: 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: Asset liability management is an important task for any insurer, where tools like Excel play an important role from an analytics perspective. Dashboard tools like Power Bi and Tableau can supercharge an organization’s ability to perform ALM with real-time precision and monitoring of risk/imbalances. The dashboard can provide insights into potential risks posed by asset allocation and concentration, allowing for informed decision-making to mitigate those risks. The presenters will walk the audience through an example of ALM use case, including how to build key performance indicators such as cash flow, asset allocation, and risk exposure. Additionally, we will demonstrate how these types of dashboards can be customized to fit the specific needs of your organization.