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: Economic Scenario Generator - Investment Section Web Series Session #2 - Real World This session will cover the following points: 1. NAIC ESG 2. Strategic Asset Allocation and Optimization Techniques 3. Covid-19 Postmortem: Modeling and calibration changes because of recent events

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: Economic Scenario Generator - Investment Section Web Series - Risk Neutral

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

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: Economic Scenario Generator - Investment Section Web Series Session #1 - Fundamentals & Hot Topics This session will cover the following points: 1. Introduction: ESG Basics, What is Risk Neutral and Real World? 2. Interest Rate Modeling 3. Equity and Private Equity Modeling 4. Example use cases facing investment professionals and actuaries today

Description: Modeling Approaches and Challenges focuses on approaches to real world simulation and the challenges and considerations in real world simulation. Topics include: A frequency domain approach to build realistic multi-horizon scenarios. (Ortec approach) Cascade models structure linking models for major asset classes and economic variables. (Moody’s approach) Overview of models, calibrations & approaches within the context of different use cases. Deterministic & stochastic approaches. Importance of model re-calibration flexibility. We will also discuss specific insights into considerations and challenges in real world simulation, which include topics like: Setting capital market assumptions and/or calibration targets. Assumptions about correlation and causality. Calibrating the model to capital markets assumptions. Validating the model against capital market assumptions. Role of history, market data and stylized facts. Importance of back testing. Calibrating the model to multiple investment horizons. Inclusion of alterative asset classes such as private equity, infrastructure, and direct real estate. Associated issues of limited data, illiquidity and greater uncertainty about calibration targets.