Description: Insurers are increasingly using advanced analytics to model expected outcomes. Two well-known examples in the life insurance industry are models for predicting policyholder behavior and to manage risk selection. The presenters will explore the various types of advanced analytics being considered in models and how the associated risks of these models can be incorporated into an enterprise's risk framework.

Description: 2021 ImpACT Conference - Session 10E: Azure Synapse for Actuaries | Solvency II Use Case 10.28.21 Recording

Description: Pricing is a central activity of actuaries to assure solvency and to manage risk. Life insurance is also a social good, not just a product, and the US population is massively underinsured. Pricing must strike a balance. Unlike a simple 4x4 matrix to produce pricing, today's technology offers the ability to evaluate literally thousands of variables, enabling a kind of precision pricing. It also heightens the possibility of introducing bias and discrimination through the use of unregulated third-party data.

Description: List of published materials on the topic of enterprise risk management.

Description: This paper discusses a test of whether ERM influences insurers’ stock market performance. Results show that stock market performance is linked to the characteristic of industry events and specific firm characteristics rather than to the success of ERM.

Description: Actuaries are increasingly being asked to incorporate Economic Scenario Generators into their work. Whether it be a Risk Neutral model for pricing or a Real-World version for Economic Capital modelling, using these tools can help produce a much more robust analysis. Unfortunately, these models and their underpinnings are also very complicated, which can lead to confusion. The presenters in this session will try to demystify some of this. In particular, they will address the following questions: • What is the difference between Real-World and Risk Neutral scenarios? • When should I use one or the other? • How are these models calibrated? • What are some of the misconceptions about these models?

Description: In order for an organization to benefit from artificial intelligence/machine learning (AI/ML) models, it requires the involvement of different business areas. During this webcast, professionals from data analytics, audit and risk areas will address challenges and provide solutions for working together to develop, implement, use and risk manage AI/ML tools. Throughout the discussion, the presenters will discuss: • Defining and exploring the challenges and objectives between data scientists, domain experts and control functions • Risk management – model risks and other risks • End-to-end model validation • Model performance vs. interpretability/understandability trade-offs • What to audit – auditor’s challenge and solutions

Description: 2021 Life Meeting - Session 6D: The Importance of Joint Credit-Market Risk Modeling 8.31.21 Recording

Description: This article is part of a 2 part series on real options analysis (ROA). Before you can understand the limitations of ROA, you must first understand the difference between risk and uncertainty. This article goes into detail explaining the vast differences between the two phenomena.

Description: This article provides background and key results of the Risk Premium Project of the Committee on Theory of Risk [DOTOR] of the Casualty Actuarial Society [CAS]. The project is intended to gather research on risk issues and encourage further development.