Description: Article discussing the benefits of the Chartered Enterprise Risk Analyst, CERA, credential.

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: The CIA, SOA and Joint Risk Management Section of the CAS, CIA, and the SOA sponsored a whitepaper on the application of ERM at a federal government level.

Description: The Society of Actuaries Individual Disability Experience Committee has completed their study of incidence experience for the period 2006-2014 comparing trends to the 2013 IDI Valuation Table (2013 IDIVT) base incidence rates. Included is the report and pivot table.

Description: Report to give actuaries practical information and guidance on LTD claim credibility for case level pricing and underwriting. Stage 2 of the GLTD Credibility Study, this analysis was expanded using the industry data gathered during Stage 1.

Description: The five-year moving average of the Actuaries Climate Index (ACI) reached a new high for the sixth consecutive quarter, according to the latest data release that includes summer 2019.

Description: The Society of Actuaries Smaller Insurance Company Section, the Financial Reporting Section, and the Committee on Life Insurance Research are pleased to make available new research investigating simplified methods for determining reserves under VM-20.

Description: This report examines lapse experience on individual disability income insurance products by various policyholder and product factors. The study can be used for industry benchmarking as well as for background information for product development and planning purposes.

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: The article introduces a cashflow matching strategic asset allocation (SAA) framework for life insurers. Given the recent shift to higher interest rates after two decades of persistent low interest rates, insurers have an opportunity to enhance their asset liability management by rebalancing asset portfolios to narrow duration gaps and attain higher risk-adjusted yields. The framework aims to align investment objectives with economic, regulatory, and rating agency requirements. It prioritizes cashflow matching as the primary optimization goal and incorporates security-level asset information to assist portfolio managers in executing SAA seamlessly. The article highlights the benefits of the cashflow matching SAA framework, including greater attention to cashflow matching, unbiased results, readily executable strategies, and stable economic reserves. A case study is included to demonstrate the effectiveness of the framework in improving risk-adjusted return and cashflow matching for a representative liability block.