Description: The document is a report on U.S. Population Mortality Observations updated with 2022 experience by R. Jerome Holman, FSA, MAAA. It covers mortality trends, analyses by factors like sex, age group, and cause of death, and includes methodological details for the data presented.

Description: This document shows you how to set up modeling platforms and start to work with them. Used in conjunction with Complexity Science - An Introduction and Invitiation for Actuaries.

Description: The optional phase-in period for life principle-based reserves (PBR) has come to a close, and companies have put in the work to bring their PBR implementations across the finish line. With the mandatory PBR implementation deadline behind them, companies can turn their attention from compliance to model and process improvements. The presenters will outline lessons learned from PBR implementations and their applications to broader use cases, as well as resources that are available for PBR modelers. Discussion topics will include items such as: Model simplifications and validation Model run-time techniques Model scalability (e.g. adding new products / future assumption updates) Sensitivity testing Model projections Available PBR & other modeling resources (e.g. PBR practice note, ASOP 56)

Description: Commentary on practical observations resulting from constructing and running Economic Capital models. Possible standardization of assumptions and result interpretation are morphing even though use of E.C. models has now been around for years.

Description: This article focuses on one fundamental modeling assumption — the choice of a benchmark rate or risk-free rate. The author looks at this bond market assumption as a case study of how one needs to monitor fundamental market changes for their impact on assumptions.

Description: For actuarial projections to serve as the management tools that they are intended to be, the persons charged with making those projections need to consider whether the projection is sufficiently sophisticated so that it does not miss major items.It is important to consider the tax effect. This article covers several of the major tax issues that should be considered in any actuarial projection.

Description: Machine learning models are not black-boxes; these models can easily be interpreted using tools like SHAP, LIME and Anchor. These tools can facilitate the understanding of the model and the business problem. We can used these tools to build better models and communicate how predictions are generated. This article demonstrates their use in a mortality model.

Description: The purpose of this article is to revise the fuzzy regression portions of earlier studies and to discuss issues related to the Tanaka approach, including a consideration of fuzzy least-squares regression models. From Actuarial Research Clearing House, 2006.

Description: Automobile insurers classify the risks that they underwrite in order to subdivide the portfolio into classes of risks with similar profiles. While some insurers may have sufficient historical data, several others may not have significant volume of experience data in order to produce reliable claims predictions to help enhance their risk classification systems. A database containing a pooled experience of several insurers thereby helps to produce a more fair, reliable, and equitable premium structure for all risks concerned. This paper uses multilevel models to analyze the data on claim counts provided by the General Insurance Association of Singapore, an organization consisting of most of the general insurers in Singapore. The multilevel nature of the data is due to the following: a certain vehicle is observed over a period of years and is insured by a particular insurance company under a certain 'fleet' policy. The authors show how intercompany data lead to a priori premiums and a posteriori corrections to these initial premiums. Specific focus is made in understanding the intercompany effects using various count distribution models [Poisson, negative binomial, zero–inflated and hurdle Poisson]. The performance of these various models is compared the authors also investigate how to use the historical claims of a company, fleet and/or vehicle in order to correct for the premium initially set.

Description: Recent articles addressed the subject of fuzzy regression [FR] and the term structure of interest rates [TSIR]. Their approach relied on possibilistic regression. Although possibilistic regression has been used in many applications, it has a number of limitations, not the least of which is its nebulous relation to the least-squares concept. As an alternative to possibilistic regression, this paper uses fuzzy least square regression to investigate the TSIR.