Description: This research report educates the actuarial community about the various shades of the flood risk as well as the state of the art in quantitative flood risk modeling.

Description: This report is a technical paper on a Predictive Analytics Framework and Reusable Workflow for Experience Analysis in actuarial studies. It covers data analysis, machine learning models, methodologies, and model interpretation techniques.

Description: There is value in a framework that allows a smooth transition from a qualitative assessment to a quantitative analysis as entities mature in their understanding of climate risk. In this report, a case study on climate change impact on home prices is used to illustrate a Bayesian network iterative approach to building a model for scenario analysis.

Description: This report provides insights into statistical methods for imputing race and ethnicity in the context of insurance industry disparities. It discusses various imputation techniques, performance metrics, and the evolution of methods over time. The aim is to address the lack of consistent data collection on race and ethnicity in insurance datasets.

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: This report provides a toolkit for actuaries and other interested parties to model and forecast mortality by cause and socioeconomic factors.

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: 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.