Description: The Excel spreadsheet contains the supporting source lapse rates for figures within the U.S. Individual Life Insurance Persistency report. The data for this report, and the Excel spreadsheet, represent experience years 2009-13.

Description: Deep learning neural network applied to experience analysis for post-level term lapse and mortality.

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: 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: This report presents the results of individual life insurance lapse experience in the United States between observation years 2009 and 2013. Lapse experience is analyzed for whole life, term life, universal life, and variable universal life plans issued between 1918 and 2012. Results for most key policy and product factors are examined.

Description: CMS provides a significant, and growing amount of information that can be leveraged by actuaries. Between the public use and research files CMS makes publicly available large amounts of information that can be assimilated to begin to understand the who, what, when, where, and how of Medicare. This session will focus on the depth and breadth of data available, summarize any restrictions on the use of the data, and identify areas where access to the new data can provide more meaningful insights than CMS's historical data sets.

Description: A short article looking at the results from an annual assumption survey of VA writers with a focus on changes companies observed and planned to make due to COVID-19. In the article we discuss what companies should be considering as they approach the 2020 year end assumption setting and audit season.