Description: This is a study to perform a mortality analysis of the 1898-1902 birth cohort.

Description: This report describes two methods, an observation-based analysis and an actual to model analysis, to analyze long-term care insurance experience to determine if calendar year trends exist.

Description: Experience report for life insurance policies with face amounts of $1 million or higher.

Description: This report highlights major weather stories that unfolded across the month of June 2020.

Description: This report looks at identifying drivers of systemic risk to China’s insurance industry and develops a measurement method for systemic risk as well as systemic risk dependence between the insurance industry and other industries.

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

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: The estimation of adequate reserves for outstanding claims is one of the main activities of actuaries in property/casualty insurance. The need to estimate future claims has led to the development of many loss reserving techniques. This paper is about the situation that occurs in claims reserving when there are negative values in the development triangle of incremental claim amounts. Typically, these negative values will be the result of salvage recoveries, payments from third parties, total or partial cancellation of outstanding claims due to initial overestimation of the loss or to possible favorable jury decision in favor of the insurer, rejection by the insurer, or just plain errors. Some of the traditional methods of claims reserving, such as the chain-ladder technique, may produce estimates of the reserves even when there are negative values. However, many methods can break down in the presence of enough [in number and/or size] negative incremental claims if certain constraints are not met. Historically the chain-ladder method has been used as a gold standard [benchmark], due to its generalized use and ease of application. A method that improves on the gold standard is one that can handle situations where there are many negative incremental claims and/or some of these are large. This paper presents a Bayesian model to consider negative incremental values, based on a three parameter log-normal distribution. The model presented here allows the actuary to provide point estimates and measures of dispersion, as well as the complete distribution for outstanding claims from which the reserves can be derived. The model applies Markov Chain Monte Carlo [MCMC] using the package WinBUGS.

Description: Using data from the Health & Retirement Study [HRS], a two-part medical claim prediction model is estimated for a cohort of survey respondents approaching Medicare-eligibility age. A High Deductible Health Plan [HDHP] with a companion Health Savings Account [HSA] is applied to simulated claims streams to model the build-up of HSA assets over the near-retirement years. Results show that the HSA is a grossly inadequate savings vehicle to provide for retiree medical care expenses at age 65. Moreover, HSA adequacy worsens exponentially with diminishing health status. Based on this simulation, it is apparent that Baby Boomers cannot adequately provide for the costs of medical care in retirement using HSAs exclusively.

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.