Akademik Veri Yönetim Sistemi
Applied Soft Computing, cilt.190, 2026 (SCI-Expanded, Scopus)
Evaluating the performance of insurance brokerage firms involves multiple, often conflicting criteria influenced by uncertainty and data ambiguity. Conventional multi-criteria decision-making (MCDM) approaches often fail to capture both preference fuzziness and the robustness of ranking outcomes. This study aims to develop a hybrid MCDM framework that ensures transparent, stable, and interpretable performance evaluation of leading global insurance brokers. The proposed framework integrates Fuzzy LBWA for deriving criteria weights with a modified ARTASI method for ranking alternatives. A dataset of 30 financial and operational indicators from 2021 to 2024 is analyzed for five major brokerage firms. Additionally, Decision Stability Intervals (DSI) and Monte Carlo simulations are introduced to test robustness and identify rank-sensitive indicators. The results reveal a consistent performance hierarchy—MMC maintains the leading position, BRO demonstrates improvement, and WTW shows notable volatility. Quantitatively, the DSI analysis revealed that liquidity ratios such as Current Assets / Total Assets and Cash Ratio have the widest stability ranges (up to +0.34 and +0.30, respectively), whereas profitability indicators exhibit the narrowest tolerance bands (approximately ±0.006–0.008), confirming their critical influence on ranking stability. Profitability and leverage indicators have the narrowest tolerance ranges, underscoring their strategic role in performance differentiation. The study contributes a novel robustness analysis tool (DSI) and demonstrates the model's adaptability across industries such as banking, energy, and logistics. It enhances managerial decision-making by identifying stability-critical metrics and offers a foundation for future research integrating behavioral and ESG-oriented factors.