Magnitude Of Claims Research Articles

Methodological Background of the New Motor Third-Party Liability Insurance Index of the Magyar Nemzeti Bank

In order to provide an accurate description of developments in the Hungarian motor third-party liability insurance (MTPL) market, as well as to inform the public and stimulate competition, the Magyar Nemzeti Bank (the Central Bank of Hungary, MNB) has elaborated an index to indicate the MTPL premium level. The method offers a comprehensive picture of changes in average premiums, as it uses data from the central itemised MTPL database to cover not only the population switching insurers but also remaining contracts and new entrants. It reduces bias due to seasonal effects and trends by eliminating changes in the stock composition. It can also illustrate how much the premium has changed in relation to the change in the magnitude of claims, taking into account the estimated average change in claims. In our study, we present the statistical and methodological considerations used in the calculation of the MTPL index and describe the relevant characteristics of Hungarian MTPLs.

Development and application of a probabilistic method for wildfire suppression cost modeling

Wildfire activity and escalating suppression costs continue to threaten the financial health of federal land management agencies. In order to minimize and effectively manage the cost of financial risk, agencies need the ability to quantify that risk. A fundamental aim of this research effort, therefore, is to develop a process for generating risk-based metrics for annual suppression costs. Our modeling process borrows from actuarial science and the process of assigning insurance premiums based on distributions for the frequency and magnitude of claims, generating parameterized probability distributions for fire occurrence and fire cost. A compound model of annual suppression costs is built from the coupling of a wildfire simulation model and a suppression cost model. We present cost modeling results for a set of high cost National Forests, with results indicating variation in expected costs due to variation in factors driving financial risk. We describe how our probabilistic cost models can be used for a variety of applications, in the process furthering the Forest Service's movement towards increased adoption of risk management principles for wildfire management. We review potential strengths and limitations of the cost modeling process, and conclude by discussing policy implications and research needs.

Partnering: Tool for Construction Claims Reduction

Alternative dispute resolution ~ADR! is a common term in the twenty-first century and especially for those involved in construction projects. Commercial construction projects are also on the rise nationwide as developers acquire more property and transform once barren land into large arenas, skyscrapers, and office buildings. Due to the increase in the demand for general contractors to compete and ‘‘win’’ the best job for the lowest price, dispute resolution is gaining importance in maintaining success for the general contractor by reducing the chance of claims for the contractor and the owner. A form of dispute resolution for construction, which is quite prevalent today, is partnering. Partnering is a process that brings the parties of the construction project together to resolve potential issues. The hypothesis of this paper is that partnering will reduce the overall number and size of claims on a construction project. By providing a framework for resolving the issues early in the construction process, it becomes more likely that conflict will not arise within the parties. The objective of this paper was to determine whether the relationship between partnering in construction projects influences the number and magnitude of claims that are filed or paid by the contractor and owner. Before one can develop an effective partnering methodology, one must understand the major reasons relating to employee dissatisfaction with current partnering methods. This study will provide an insight into partnering and its effects on the number and magnitude of claims as well as suggestions for more effective partnering methodology.

Theory & Methods: A zero‐augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non‐degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero‐augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.

Auto Insurers and the Airbag: Reply

Peterson and Hoffer (1996) provide interesting evidence of offsetting behavior and other useful data in their comment on our article (Kneuper and Yandle, 1994). We welcome their effort test hypotheses related our research on airbags. Their comment provides an opportunity for us clarify a key argument in our piece and note the linkages between our argument and those of Peterson and Hoffer. Our article describes airbag technology as a means for auto insurers to change the nature of the automobile hazard, effectively alter the nature of losses and convert uncertainty of future claims into (p. 108). We discuss the possibility that the dollar value of property claims might rise if airbags shift personal injury losses property losses, but note that predictability is what matters, not the magnitude of claims. It is in this context that we call attention Peltzman's work on offsetting behavior, noting that, under our theory, insurers prefer predictable risk uncertainty even if improved auto safety induces riskier behavior (footnote 7, p. 113). The ability set accurate premiums matters most. Given a population of seatbelt-equipped cars, insurers cannot ex ante predict accurately the behavior of drivers. Therefore, ex post variance or risk associated with claims will be higher. Risk management requires risk estimation. Mandatory airbags move the population of autos in the direction of homogeneous exposure units and convert uncertainty, which cannot be estimated, risk, which can. Following our discussion, Peterson and Hoffer see the possibility that conversion airbags will change the frequency of claims as between personal injury and property losses. They test the hypothesis that the ratio of claims for property loss personal injury claims will be higher for a population of airbag-equipped cars. They find that the frequency for both categories of claims rises and present suggestive evidence that property loss claims may rise faster than personal injury claims. The tentative evidence could be seen as being consistent with our argument about loss substitution.