Description: An article presenting updated results as of 12/2021 by using analgorithm that formed part of a paper presented at the AFIR-ERMColloquium which took place in Panama, in 2017. This article starts by proving that the conditions that have to be met in order for the formula to apply that were present in 12/2015 still hold.

Description: The growing global recognition of the importance of climate-related risks (transition and physical) has triggered a significant demand for research on the interface between climate and actuarial sciences, for example on the impact of risks from climate change on insurance markets and their operations. Furthermore, the systemic nature of climate-related risks underpins actuaries’ need to engage and work with diverse groups of stakeholders, including clients, communities, governments, regulators, boards, and investors. Contributions are invited for a special issue of the Annals of Actuarial Science featuring research on the topic ‘Managing Climate-Related Risks’. This special issue aims to capture leading interdisciplinary thinking, with clear relevance to actuarial problems in pensions and insurance.

Description: In this article, we introduce a novel machine learning-based model that analyzes and predicts policyholder behaviors at the individual policy level. Policyholder embedding representations are learned using a Recurrent Neural Network (RNN) that takes into account the sequence of historical transactions associated with a policy. Additionally, a Neural Network with a multi-head architecture is trained to predict the policyholder's next action based on the policyholder embedding and other policy attributes. The resulting model offers several advantages over traditional experience studies and assumption-setting methods, including the ability to set policy-level assumptions that improve reserve accuracy, predict multiple behaviors simultaneously while capturing the interrelationships between them, and generate policyholder embedding representations that are useful for segmentation.

Description: Artificial intelligence (AI) at the heart of new digital tools allows actuaries to extract structured data useful for creating more precise risk assessment models. This technology sits best alongside the expertise of insurance professionals who have years of experience in the industry. RGA’s partnership with DigitalOwl is an example of how AI combines with human knowledge for the best outcomes.

Description: After years of discussion and debate, PrinciplesBased Reserving (PBR) is finally a reality. Small insurance companies have a unique opportunity to decide between electing for the company-wide exemption or adopting the PBR methods. This article discusses the benefits and challenges of the options available to small insurers under VM-20 as well as best practices that have been found along the way.

Description: In this SOA e-course, you'll learn how to develop an enterprise risk management (ERM) framework and help fulfill your SOA CPD requirement at the same time.

Description: This article enumerates many important considerations for a managing general underwriter(MGU) desiring to contract with an insurance carrier to issue and service provider excess of loss policies covering catastrophic medical risks assumed by accountable care organizations (ACOs).

Description: Lifetime pension pools convert a lump sum into lifelong income, periodically adjusting benefit payouts based on mortality and investment experience. Volatility-targeting strategies can enhance benefit stability by dynamically adjusting the investment portfolio’s exposure to risky assets in the context of these pools, demonstrating superior performance compared to traditional static asset allocation methods.

Description: Global trends in both technology and regulation are causing U.S. insurers to reflect more deeply on their model risk management frameworks. The world has seen a rapid proliferation of AI-based tools accompanied with an overall increased usage of machine learning models, exacerbating model risk and reducing transparency in an unprecedented manner.