Description: Many ERM programs fail to include an active step of actual risk treatment and are therefore rightfully characterized as Entertainment Systems. Real ERM will use risk assessment to drive decisions about risk acceptance and risk mitigation. Entertainment systems use risk assessment to create reports and to hold discussions. No actions are taken often because ERM is not empowered to do anything.

Description: Advanced Topics in Corporate Finance and Enterprise Risk Management (ERM) covers a broad range of corporate finance concepts. As part of a 10-year strategic plan exercise, you'll apply opportunity engineering to help guide senior management in introducing a new product to market. You'll also focus on three applications of Extreme Value Theory (EVT), which are covered and tested in a business context.

Description: While COSO describes guiding principles for a comprehensive and robust ERM framework, it is anything but prescriptive. This article describes one path toward the realization of a framework consistent with COSO and highlights several challenges and solutions.

Description: The surge in interest rates over the past few years presents opportunities for life insurers to enhance their Asset and Liability Management (ALM) positions through asset rebalancing to narrow the duration gap and secure higher risk-adjusted yields. This webcast introduces a robust, efficient and readily-executable, liability-driven strategic asset allocation (SAA) framework and implementation considerations. At the conclusion of the webcast, attendees will learn about the key objectives and practical considerations for a successful SAA.

Description: Reunite with members and non-members in the China actuarial community to discuss recent topics and related issues in areas such as macroeconomic sharing, IFRS 17, product strategy, cross-border communication and discussion, and more. Advance your career through learning and networking opportunities, and share your experiences in the finance and insurance industries, as well as other fields related to actuarial science.

Description: Hear about modernized reporting capabilities and technical analysis, which is enabled through access to deep actuarial and financial planning data. The webcast will focus on a real post-Long Duration Targeted Improvements (LDTI) source of earnings analysis and on a high-level LDTI forecasting capability.

Description: 2011 Enterprise Risk Management Symposium, Chicago. This paper explores cognitive and behavioral theories that can be applied to explain why emerging risk is often overlooked, downplayed or ignored. It also proposes an innovative framework for managing emerging risk within an overall enterprise risk management program.

Description: In this article, the interviewers will discuss the pros and cons of centralized modeling function vs a business unit based modeling function. Their experience will help us in identifying factors in contributing to a successful centralized model function and understand how to set expectation with modeling stakeholders.

Description: Learn how modernization of data, models, and technology are influencing workflows. This webcast will address how workflows are changing in response to new regulatory requirements and the impacts the changes have on modeling and end-to-end future state operating models. Industry leading actuaries will discuss: How team collaboration can increase quality and speed of reporting to internal and external stakeholders How to strengthen controls and compliance frameworks across finance and actuarial departments How to modernize actuarial input/output for strategic analysis and insights How to attract and retain top talent by improving opportunities for actuaries There will also be dedicated time to ask questions of those who have made the transition.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.