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: You’ve written the position papers, updated the models, and designed the required disclosures, but you may be wondering how to explain the results during interim financial reporting periods to company management as well as other stakeholders. This session will explore ways to adapt existing management reporting and offer ideas for new metrics. We will explore various methods for analyzing DAC amortization patterns, market risk benefit reserve patterns, and Liability for Future Benefits (LFPB) movements. In addition, this session will explore ways to integrate changes in LDTI with business planning and other broader business issues such as product pricing considerations and reinsurance strategy.

Description: The frequent use and complexity of today's actuarial projection models have increased the importance of efficient models. Model optimization can reduce both cloud costs and overall end-to-end clock times. This, in turn, enables companies to do more with less, such as reducing close times, increasing controls, enhancing analysis, doing more complicated calculations, and/or improving employee workloads during the close. From design and data to execution and audit, join us to explore techniques that can be applied throughout the actuarial modeling process to optimize actuarial models for improved run-time, accuracy, and reliability. We will focus on techniques that don't rely on model compression.