Description: This study explores the use of geospatial imagery and high-resolution image technology in the property and casualty (P&C) insurance industry, focusing on improving risk determination through accurate building location data. It introduces new methods like structure footprints and high-resolution elevation information to enhance underwriting, rating, and claims settlement. The study includes two case studies: one on flood risk in Hillsborough County, Florida, and another on wildfire risk in three California communities, demonstrating the significant impact of precise location and elevation data on risk assessment and insurance premiums.

Description: This report analyzes insurance product design, morbidity trends, and pricing models for individuals with pre-existing illnesses in China to improve coverage for substandard health populations.

Description: This report outlines a methodology for estimating mortality improvement factors based on data from insured lives. The report details challenges, methodology, results, comparisons with population data, limitations, and potential improvements.

Description: The Product Development Section, the Financial Reporting Section, the Modeling Section and the Committee on Life Insurance Research are pleased to present new research on modeling and forecasting cause-of-death mortality. Authored by a Milliman team led by Alexandre Boumezoued, the report develops a framework for forecasting cause specific death rates. Included also is a Cause of Deaths Forecasting Tool and a user guide for the tool.

Description: This research report is a summary of an online survey and follow-up discussions with survey respondents on the current state of actuarial modeling controls within U. S. and Canadian life insurance and annuity companies. The current state was then compared to the results from the 2012 survey on this topic to examine the evolution in actuarial model controls and governance. The report also provides an industry assessment and proposes considerations for enhancing the current state towards a well-controlled governance framework.

Description: This paper explores areas where fuzzy logic models may be applied to improve risk assessment and risk decision-making. It discusses the methodology, framework and process of using fuzzy logic systems for risk management. With the help of practical examples, it is hoped that it will encourage wise application of fuzzy logic models to risk modeling.

Description: Life insurance industry use experience studies to manage and monitor their blocks of business. The purpose of this paper is to provide an overview of the data cleansing and validation role specific to the life insurance experience study process.

Description: A fuzzy random variable [FRV] has been conceptualized as a vague perception of a crisp but unobservable random variable [RV] and also as a random fuzzy set. Since actuaries seem receptive to these notions of FRV, and there are sources of uncertainty that RVs can not accommodate, but FRVs can, one would expect FRVs to be a component of the actuarial arsenal, but generally this is not the case. Assuming that the reason for this is that potential users are not sufficiently familiar with FRV methodology, the purpose of this article is to help alleviate this situation by presenting a FRV primer.

Description: In the classical risk theoretic model with Poisson claim arrival and exponential claims, consider the discounted value of claims nos. 3, 6, 9, and so on. [To risk theory specialists, considering every third claim in a Poisson arrival process is the same as assuming waiting times are Erlang[3] in a Sparre-Andersen model.] The distribution of the discounted value of future claims can be found. It turns out that this is the same as the distribution of the product of two independent variables, one having a gamma distribution, the other a 'complex-parameter beta product distribution'. The latter will be defined, it involves extending the usual beta distribution.

Description: This presentation discussed the Copula Methodology, which is the most popular methodology in multivariate modeling in finance and insurance. The definition, advantages, limitations, and uses of the Copula Methodology were presented. Some other multivariate models were discussed as well. From Actuarial Research Clearing House, 2012, Vol. 1.