Description: Chapter 1 presents the background to the current study, followed by the methodology used, and a summary of the findings of the study. From Group Medical Insurance Large Claims Database Collection and Analysis, M-HB97-1.

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

Description: This report presents research into recent population mortality experience compiled by the Retirement Plans Experience Committee of the Society of Actuaries Research Institute as it pertains to development of mortality improvement assumptions for measuring obligations of retirement programs in the United States.

Description: This report presents research into recent population mortality experience compiled by the Retirement Plans Experience Committee (RPEC) of the Society of Actuaries Research Institute as it pertains to development of mortality improvement assumptions for measuring obligations of retirement programs in the United States.

Description: A data format for mortality tables along with applications.

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: Guidance and a model for actuaries to use when performing their professional services related to Medicaid (Title XIX) and Children’s Health Insurance Program (CHIP or Title XXI) managed care capitation rate setting.

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: This project enhances the current understanding of cyber risks in the smart home ecosystem from the insurance industry's perspective. In particular, the quantitative framework and pricing strategies developed in this project can be immediately adopted/adapted by actuaries to price the cyber risks for smart homes, a fast-growing insurance market.

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.