The future values of the expected claims are very important for the insurance companies for avoiding the big losses under uncertainty which may be produced from future claims. In this paper, we define a new size-of-loss distribution for the negatively skewed insurance claims data. Four key risk indicators are defined and analyzed under four estimation methods: maximum likelihood, ordinary least squares, weighted least squares, and Anderson Darling. The insurance claims data are modeled using many competitive models and comprehensive comparison is performed under nine statistical tests. The autoregressive model is proposed to analyze the insurance claims data and estimate the future values of the expected claims. The value-at-risk estimation and the peaks-over random threshold mean-of-order-p methodology are considered.

A novel flexible extension of the Chen distribution is defined and studied in this paper. Relevant statistical properties of the novel model are derived. For the actuarial risk analysis and evaluation, the maximum likelihood, weighted least squares, ordinary least squares, Cramer–von Mises, moments, and Anderson–Darling methods are utilized. For actuarial purposes, a comprehensive simulation study is presented using various combinations to evaluate the performance of the six methods in analyzing insurance risks. These six methods are used in evaluating actuarial risks using insurance claims data. Two applications on bimodal data are presented to highlight the flexibility and relevance of the new distribution. The new distribution is compared to several competing distributions. Actuarial risks are analyzed and evaluated using actuarial data, and the ability to disclose actuarial risks is compared by a comprehensive simulation study, through which actuarial disclosure models are compared using a wide range of well-known models.

Assessing the probability of very low or high water levels is an important issue in the management of hydroelectric dams. In the case of the Akosombo dam, very low and high water levels result in load shedding of electrical power and flooding in communities downstream respectively. In this paper, we use extreme value theory to estimate the probability and return period of very low water levels that can result in load shedding or a complete shutdown of the dam’s operations. In addition, we assess the probability and return period of high water levels near the height of the dam and beyond. This provides a framework for a possible extension of the dam to sustain the generation of electrical power and reduce the frequency of spillage that causes flooding in communities downstream. The results show that an extension of the dam can reduce the probability and prolong the return period of a flood. In addition, we found a negligible probability of a complete shutdown of the dam due to inadequate water level.

Richard Minkah, Tertius de Wet, Abhik Ghosh, Haitham M. Yousof. CSAM 2023;30:531-50. https://doi.org/10.29220/CSAM.2023.30.6.531