Optimizing insurance risk assessment: a regression model based on a risk-loaded approach

Zinoviy Landsman, Tomer Shushi

Research output: Contribution to journalArticlepeer-review

Abstract

Risk measurement and econometrics are the two pillars of actuarial science. Unlike econometrics, risk measurement allows taking into account decision-makers’ risk aversion when analyzing the risks. We propose a hybrid model that captures decision-makers’ regression-based approach to study risks, focusing on explanatory variables while paying attention to risk severity. Our model considers different loss functions that quantify the severity of the losses that are provided by the risk manager or the actuary. We present an explicit formula for the regression estimators for the proposed risk-based regression problem and study the proposed results. Finally, we provide a numerical study of the results using data from the insurance industry.

Original languageEnglish
JournalAnnals of Actuarial Science
DOIs
StateAccepted/In press - 1 Jan 2024

Keywords

  • Loss functions
  • optimization
  • penalty loss function
  • regression theory
  • trade-off coefficient

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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