A New Reciprocal Weibull Extension for Modeling Extreme Values with Risk Analysis under Insurance Data

Probability-based distributions might be able to explain risk exposure well. Usually, one number, or at the very least, a limited number of numbers called key indicators (KRIs), are used describe level exposure. These values, which undeniably outcome specific model, frequently referred as essential critical indicators. Five indicators, including value-at-risk, tail variance, tail-value-at-risk, and mean-variance, were also for describing under reinsurance revenues data. measurements created proposed model; hence, this paper presents novel distribution purpose. Relevant statistical properties derived, generating function, ordinary moments, incomplete moments. Special attention is devoted applicability new model extreme data sets. Three applications real show usefulness adaptability model. The proved its superiority against many well-known related models. employed analyzing dataset. An application provided along with relevant numerical analysis panels. Some useful results identified highlighted.