Description: Many companies are facing challenges with their inforce UL business. These “distressed” or non-core blocks of business can be a drag on earnings, return on capital, and/or internal resources. With the pressures of today's marketplace, companies are seeking efficient strategies for managing these blocks. The good news is that strategies exist such that inforce UL business can be optimized with some attention. Learn about some options and levers companies have available to help manage these blocks.

Description: For insurers to preserve and create value, they must understand their exposure to carbon and climate risks and develop decarbonization strategies. Transition plans facilitate this by encouraging organizational thinking and disclosure to the market. 2023 is a crucial year for progress on these initiatives amid evolving regulatory expectations. This session will provide an overview of the transition planning process, value creation and drivers identification, and how to incorporate these into transition and resiliency strategies. As part of climate risk mitigation strategies during transition and beyond, we will present a data and technology framework for insurers to more effectively manage climate risk. In particular, we will discuss dynamic climate risk management (DCRM) solutions which use data and technology to inform adaptation and resilience. Emerging technologies such as AI, IoT, and blockchain can also help insurers manage risks related to climate change more effectively. By attending the session, attendees will learn how to develop transition plans, adopt DCRM solutions and leverage emerging technologies to build resilience against the challenges caused by climate change.

Description: Internal modernization initiatives and external changes of the financial reporting regulations (e.g., LDTI, IFRS 17) increase the amount of validation activities for actuarial models. In addition, the use of predictive analytics in actuarial modeling requires a different set of validation skillsets/techniques in model validation. These internal and external forces call for a structured, streamlined and robust model validation practice.