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Yazar "Meral, Hasan" seçeneğine göre listele

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    Closing the Insurance Gap in Turkey: Prioritizing Key Factors and Strategies
    (WESTERN RISK & INSURANCE ASSOC, 821 ACADEMIC WAY, 313 ROVETTA COLL BUSINESS, FLORIDA STATE UNIV, TALLAHASSEE, FL 32306-1110, 2023) Meral, Hasan; Ersoy, Behlül; Çavga, Seyit Hamza
    Turkey presents a unique opportunity for improving insurance coverage, thanks to its dynamic economy, high working-age population, and strategic geopolitical location. Despite its significant economic potential, insurance penetration in Turkey is relatively low compared to other emerging nations. This study aims to identify the primary indicators and prioritize investment areas to improve Turkey's insurance penetration. The study employs the AHP method to weight selected criteria based on a literature review, relying on expert opinions. Subsequently, the TOPSIS method is used to rank the alternative results. The findings indicate that probability and level of competition are the most critical factors determining insurance coverage in Turkey. Additionally, technological transformation and intellectual capital are the most important investment areas to increase penetration in the country, while innovation is the least essential alternative. The results of our study can serve as a valuable reference point for industry stakeholders and policymakers, especially in economies struggling with low insurance penetration. This study presents a roadmap to narrow the insurance coverage gap by identifying and prioritizing strategic investment opportunities while optimizing investment returns.
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    The role of ESG indicators in closing the disaster insurance gap: a machine learning analysis
    (TAYLOR & FRANCIS LTD, 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND, 2025) Meral, Hasan; Çavga, Seyit Hamza
    This study investigates the role of environmental, social, and governance (ESG) indicators in addressing global protection gaps in disaster insurance. Utilizing a comprehensive dataset from EM-DAT and the World Bank, covering the period from 2000 to 2022, the research employs advanced machine learning techniques to analyze the complex relationships between ESG factors and disaster insurance coverage. Among the methodologies applied, CatBoost and Gradient Boosting stand out for their strong predictive performance and reliable generalization capabilities. The findings reveal that governance quality, particularly in terms of stronger control of corruption, is the most significant ESG factor. On the social dimension, improved access to essential infrastructure, emerges as a crucial contributor to disaster insurance coverage. Additionally, environmental conditions related to the economic significance of primary sectors help elucidate variations in coverage. Alongside these ESG variables, disaster-specific factors, particularly total economic damage, remain critical determinants of protection gaps.

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