Browse Research
Viewing 26 to 50 of 7695 results
2023
Method
We use foundational actuarial concepts to build a full stochastic model of casualty catastrophe (cat) risk.
Results
Casualty cat modeling is achievable within the actuarial community.
Conclusions
An actuarial approach can be used to build a forward-looking stochastic casualty cat model.
2023
P&C insurers who provide coverages subject to premium audit are exposed to elevated uncertainty in the amount and timing of future revenue and cash flow. Audit premiums can vary significantly from expectations when the path of the economy is uncertain or rapidly changing.
2023
In this paper, excess severity behaviors of Pareto-Exponential and Pareto-Gamma mixture are examined. Mathematical derivations are used to prove certain properties, while numerical integral computations are used to illustrate results.
2023
Generalized Linear Models (GLM) have become an insurance industry standard for classification ratemaking. However, some of the technical language used in explaining what a GLM is doing in its calculation can be obscure and intimidating to those not familiar with the tool. This paper will describe the central concept of GLM in terms of the estimating equations being solved; allowing the model to be interpreted as a set of weighted averages.
2023
The CAS has made a number of statements about DE&I and systemic racism in insurance, including the series on race and insurance. This paper argues that the the CAS papers do not make a compelling case and that the differentation in pricing today is appropriate and reflects real differences in risk.
2023
This paper will examine the issue of social inflation trend in Medical Malpractice indemnity payments. I will demonstrate how a Calendar Year (CY) paid severity trend model can be used to identify and project social inflation. Using publicly available data, I will show how this line of business exhibits a cyclical stair step pattern which is the cornerstone of what I call the Level Shift model.
2023
Two major regions devastated by climate change are Africa and Asia. However, little is known about the characteristics of the different compound climatic modes within the specified regions which is key to managing climate risks. The joint behavior of mean rainfall and temperature in Nigeria, South Africa, Ethiopia and India are thus studied in this paper.
2023
Many actuarial tasks, such as analysis of pure premiums by amount of insurance, require an analysis of data that is split among successive “buckets” along a line. Often, there is also significant randomness in the data. That results in process error volatility that affects the (usually average) values of the data within the buckets, so some smoothing of these values is needed if they are to be truly useful.
2023
This project enhances the current understanding of cyber risks in the smart home ecosystem from the insurance industry's perspective. In particular, the quantitative framework and pricing strategies developed in this project can be immediately adopted/adapted by actuaries to price the cyber risks for smart homes, a fast-growing insurance market.
2022
This paper defines several terms that are currently being used in discussions around potential discrimination in insurance – protected class, unfair discrimination, proxy discrimination, disparate impact, disparate treatment, and disproportionate impact – and provides historical and practical context for them. It also illustrates the inconsistencies in how different stakeholders define these terms.
2022
This paper examines four commonly used rating factors in personal lines insurance – credit-based insurance score, geographic location, home ownership, and motor vehicle record – to understand how the data underlying insurance pricing models may be impacted by racially biased policies and practices outside of the system of insurance.
2022
This paper examines issues of racial bias in lending practice for mortgages, personal and commercial lending, as well as credit-scoring. It looks at these four areas and describes solutions intended to address any potential bias, which may include government intervention, internal bias testing and monitoring measures, and development of new products to mitigate bias.
2022
As the insurance industry focuses attention on potential racial bias across all practice areas, this paper examines three approaches to defining and measuring fairness in predictive models. It also provides an overview of several bias mitigation techniques that can be performed during the input, modeling, or output phase of a model once a set of fairness criteria has been adopted.
2022
This paper presents an approach for combining (or unifying) triangle-based reserving methods. The approach I present expresses the combination of multiple triangle-based methods as a multivariate linear model. I intend this approach to provide a more flexible model with a statistical basis for underlying actuarial assumptions and the selection of the accident year point estimate after consideration of multiple methods.
2022
To model property/casualty insurance frequency for various lines of business, the Negative Binomial (NB) has long been the distribution of choice, despite evidence that this model often does not fit empirical data sufficiently well. Seeking a different distribution that tends to provide a better fit and is yet simple to use, we investigated the use of the Zipf Mandelbrot (ZM) distribution for fitting insurance frequency.
2022
The purpose of this article is to provide a computational tool via Maximum Likelihood (ML) and Markov Chain Mont Carlo (MCMC) methods for estimating the renewal function when the inter-arrival distribution of a renewal process is single-parameter Pareto (SPP). The proposed method has applications in a variety of applied fields such as insurance modeling and modeling self-similar network traffic, to name a few.
2022
Number and location of knots strongly impact fitted values obtained from spline regression methods. P-splines have been proposed to solve this problem by adding a smoothness penalty to the log-likelihood. This paper aims to demonstrate the strong potential of A-splines (for adaptive splines) proposed by Goepp et al. (2018) for dealing with continuous risk features in insurance studies.
2022
This paper takes a deep dive into historical loss reserves. Using Schedule P company filings, it is shown that reserves are very slow to react to emerging losses, much slower than the most accurate approach would dictate. There are other concerns besides accuracy, such as stability and avoiding deficient reserves. But attempting to explain the discrepancy in this manner alone would require a level of risk aversion that is unrealistic.
2022
This paper aims to demonstrate how deep learning (a subset of machine learning) can be used to forecast the ultimate losses of a sample group of Property and Casualty insurance companies. The paper initially explores the concept of loss development - how losses incurred by an insurance company mature across time. These losses then reach a final amount, known as the ultimate loss.
2022
The use of bootstrapping methods to evaluate the variance and range of future payments has become very popular in reserving. However, prior studies have shown that the ranges produced may be too narrow and do not always contain the actual outcomes when performing back-testing. This paper will look at some ways that the ranges determined by bootstrapping methods can be made more realistic.
2022
Current methods for evaluating risk transfer, such as the ‘10/10’ rule, suffer from a few problems: They are by nature ad hoc and thus are not a direct consequence of risk transfer; they do not properly evaluate some treaties with obvious risk transfer; and they may be gamed. This paper provides alternative methods for assessing risk transfer.
2022
This paper advances the theory and methodology for quantifying reserve risk. It presents a formula for calculating the variance of unpaid losses that is based on analyzing volatility in a triangle of estimated ultimate losses. Instead of examining variability in paid or case incurred loss development, this approach focuses on the estimated ultimates.
2022
In finance, the LaPlace transform is used to calculate the distribution of stochastic present value. There are several practical impediments to the use of the LaPlace transform in actuarial science: we lack a physical interpretation of the transform, it requires a change in perspective to a frame of reference that we seldom use, and it involves complex arithmetic.
2022
The Machine Learning Working Party of the CAS identified one barrier to entry for actuaries interested in machine learning (ML) as being the fact that published research in an insurance context is sparse. The purpose of this paper is to provide references and descriptions of current research to act as a guide for actuaries interested in learning more about this field and for actuaries interested in advancing research in machine learning.
