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: Meet the authors of and learn about the contributors to the newly published e-book "Design Your Future with the Competency Framework." Presenters will share their personal reflections, stories and words of wisdom to bring to life the eight professional and personal competencies in the SOA Competency Framework.

Description: Hot Topics in Setting Pension Return Assumptions is the third in a series, following Mathematics of Pension Return Assumption and Inflation, focusing on Effects, Forecasts, and Role in Pension Valuations. What time horizon is more appropriate for Capital Market Assumption (CMA) forecasts: mid-term or long-term time horizon, or a blend of both? How do we map the asset classes of a plan's own investment policy to the asset classes of investment forecasters' CMAs? If we use asset-smoothing for valuation assets, should we use market-driven expected returns or smoothed/average versions of them?

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.