Description: Actuarial concepts of credibility can be extended to encompass additional information provided by new machine learning estimates. New statistical ideas also indicate how to generalize to blend arbitrarily many estimators, while not double-counting common information. For example, historical claim costs and risk scores both leverage diagnosis code information. We introduce two new estimators, one based on partial correlation and the other based on the machine learning-derived Shapley statistic, to address these limitations.

Description: Disability and group life products can pose challenges for the valuation actuary due to the long-tail runout of traditional disability and group life waiver of premium claims, and due to the sensitivity of the reserves to small changes in assumptions. This session addresses a number of issues of interest to actuaries who work with these products, including recent experience trends and the new 2023 Group Life Waiver Valuation Table. Attendees will learn about best practices in the valuation of these products.

Description: This article will examine four approaches under two different methods (eight in total) used to combat the non-additivity of key rates and will compare them under a number of rate environments.

Description: In recent years, vast amount of data from emerging experiences has helped insurers gain a deeper understanding of their policyholders and the factors driving their behaviors. However, due to limitations in traditional experience study methods, this understanding typically remains at the cohort level. This series of articles proposes 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: Accounting Standards Update No. 2018-12, Financial Services – Insurance (Topic 944): Targeted Improvements to the Accounting for Long-Duration Contracts (LDTI) has been effective for most large, public business entities since 2023 and will be effective for other insurers in 2025. While the guidance clarifies the need for incorporating claim liabilities and premium accrual balances in the liability for future policy benefits (LFPB) under LDTI, companies may still have questions given there are limited details available around the techniques used in practice. In this article, we demonstrate the need for incorporating claim liabilities and premium accrual balances in the LFPB under LDTI. We also look at two potential approaches of incorporating these accrual balances.

Description: As the insurance industry continues to evolve due to a pandemic and rapidly changing economic and regulatory conditions, the role of data scientists and actuaries has become increasingly important. In an interview with the Atidot data science and actuarial team, we discuss what it is like to work at an InsurTech, how data scientists and actuaries can collaborate, and look at some concrete technical examples of where the innovation is having an impact. This article explores what collaboration can look like and how the role of data science and actuary may evolve in the InsurTech industry in the future.

Description: Climate risk modeling is taking center stage. With businesses increasingly expected to show how they assess, adapt to, and mitigate the risks of climate change, companies are asking: What is the right climate risk modeling approach for our business?

Description: This article provides an alternative method to the computation of the Reduction in Benefits Test under 7702A. Part 1 provided for reduction in death benefits. Part 2 provides for reduction in Qualified Additional Benefits.

Description: The direct and indirect effects of the pandemic have impacted many actuaries’ work since the spring of 2020. This article addresses three considerations actuaries should account for when using risk evaluation models which are focused on projecting remote, highly unlikely losses: model effectiveness, asset risk, and insurance (mortality and mortality) risk.