A Hybrid Fuzzy Regression Model for Optimal Loss Reserving in Insurance

TL;DR

This paper introduces a hybrid fuzzy regression model with optimized parameters within the GLM framework to improve loss reserving accuracy in insurance, outperforming classical models in predictive performance.

Contribution

The paper develops a novel hybrid fuzzy regression model with asymmetric fuzzy coefficients and optimized h-value, enhancing loss reserving predictions in insurance.

Findings

The hybrid model outperforms classical GLM in reserve prediction error.

The model achieves lower reserve standard deviation.

Numerical results demonstrate improved accuracy in loss reserving.

Abstract

In this article, a Hybrid Fuzzy Regression Model with Asymmetric Triangular Fuzzy Coefficients and optimized $h-$value in Generalized Linear Models (GLM) framework have been developed. The weighted functions of Fuzzy Numbers rather than the Expected value of Fuzzy Number is used as a defuzzification procedure. We perform the new model on a numerical data (Taylor and Ashe, 1983) to predict incremental payments in loss reserving. We prove that the new Hybrid Model with the optimized $h-$value produce better results than the classical GLM according to the Reserve Prediction Error and Reserve Standard Deviation.