10.57647/fomj.2026.0701.05

Efficiency Analysis of Insurance Companies under Fuzzy Environment

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  • Javad Gerami*1,
  1. Department of Mathematics, Shi.C., Islamic Azad University, Shiraz, Iran

Received: 0025-12-18

Revised: 2026-02-12

Accepted: 2026-02-22

Published in Issue 2026-03-30

Copyright (c) 2026 Javad Gerami (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Gerami, J. (2026). Efficiency Analysis of Insurance Companies under Fuzzy Environment. Fuzzy Optimization and Modeling Journal (FOMJ), 7(1). https://doi.org/10.57647/fomj.2026.0701.05
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Abstract

Performance evaluation is crucial for the sustainable growth and effective resource allocation of insurance companies. However, the uncertainty and imprecision inherent in operational and financial data limit the applicability of traditional methods. This study proposes a novel fuzzy two-stage data envelopment analysis (DEA) model to assess the efficiency of insurance companies under such conditions. The model employs trapezoidal fuzzy numbers to represent inputs, intermediate measures, and outputs, thereby providing a more robust and realistic efficiency measurement than conventional deterministic DEA. Applied to a dataset of 40 insurance companies, the model yields individual stage efficiencies, overall efficiency scores, and optimal improvement targets for each firm. The key findings reveal that this approach not only identifies significant inefficiencies but also offers actionable strategic insights. Specifically, it enables managers to enhance cost control, optimize policy issuance volumes, accelerate claims settlement, and improve customer satisfaction. These improvements are essential for achieving a sustainable competitive advantage in the highly dynamic insurance market. The study concludes that the fuzzy two-stage DEA model is a valuable tool for both managers and policymakers in making informed decisions for superior resource allocation and performance management in the insurance sector.

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