HAYAT DIŞI SİGORTA SEKTÖRÜNDE FİNANSAL PERFORMANSIN ÇKKV TEMELLİ ANALİZİ: LOPCOW, CRITIC VE MAIRCA YÖNTEMLERİNİN ENTEGRE UYGULAMASI

Abstract

Çalışmada Türk hayat dışı sigorta sektörünün 2016–2023 dönemi arasındaki finansal performansını çok kriterli karar verme (ÇKKV) yöntemleri kullanarak nesnel biçimde değerlendirmeyi amaçlamaktadır. Bu kapsamda, öncelikle performans kriterlerinin ağırlıkları LOPCOW ve CRITIC yöntemleri ile ayrı ayrı hesaplanmış, ardından iki yöntemin çıktıları birleştirilerek daha dengeli bir ağırlıklandırma elde edilmiştir. Nihai olarak, MAIRCA yöntemi kullanılarak her yılın performansı hesaplanmış ve dönemsel sıralama yapılmıştır. Analizlerde brüt prim üretimi, ödenen tazminat, teknik kâr, bilanço kârı, sermaye yeterlilik rasyosu, özsermaye kârlılığı ve aktif kârlılığı gibi yedi temel finansal gösterge esas alınmıştır. Elde edilen bulgular, 2023 yılının sektör açısından en yüksek performansa sahip yıl olduğunu, 2016 yılının ise en düşük performansı gösterdiğini ortaya koymuştur. Ayrıca, ödenen tazminat kriteri hem LOPCOW hem de CRITIC yöntemlerine göre en yüksek ağırlığa sahip performans belirleyici unsur olarak öne çıkmıştır. Bu sonuçlar, sigorta şirketlerinin finansal sağlamlıklarının artırılması ve sürdürülebilir rekabet avantajı sağlanması açısından tazminat yönetimi, sermaye yapısı ve kârlılık gibi unsurların stratejik önemini vurgulamaktadır. Çalışma, ÇKKV tabanlı metodolojik yaklaşımıyla hem akademik literatüre katkı sunmakta hem de sektör aktörlerine karar destek perspektifi sağlamaktadır.

Keywords

• Sigortacılık Sektörü
• Hayat Dışı Sigortacılık
• LOPCOW
• CRITIC
• MAIRCA

MULTI-CRITERIA DECISION-MAKING MODEL BASED ANALYSIS OF FINANCIAL PERFORMANCE IN NON-LIFE INSURANCE SECTOR: INTEGRATED APPLICATION OF LOPCOW, CRITIC AND MAIRCA METHODS

Abstract

This study aims to objectively evaluate the financial performance of the Turkish non-life insurance sector for the period 2016-2023 using multi-criteria decision making (MCDM) methods. In this context, firstly, the weights of the performance criteria were calculated separately by LOPCOW and CRITIC methods, and then the outputs of the two methods were combined to obtain a more balanced weighting. Finally, each year's performance was calculated using the MAIRCA method and a periodical ranking was made. The analysis is based on seven key financial indicators such as gross premium production, claims paid, technical profit, balance sheet profit, capital adequacy ratio, return on equity and return on assets. The findings revealed that 2023 was the year with the highest performance for the sector, while 2016 was the year with the lowest performance. Moreover, the claims paid criterion stands out as the performance determinant with the highest weight according to both LOPCOW and CRITIC methods. These results emphasize the strategic importance of claims management, capital structure and profitability in enhancing the financial soundness of insurance companies and ensuring sustainable competitive advantage. The study contributes to the academic literature and provides a decision support perspective to the sector actors with its methodological approach based on MCDM.

Keywords

• Insurance Sector
• Non-Lıfe Insurance
• LOPCOW
• CRITIC
• MAIRCA

References

1. Ahmed, S., ve Khan, M. (2018). Multi-criteria decision analysis for insurance fraud risk assessment. Risk Management and Insurance Review, 21(2), 235-260.
2. Akbulut, O. Y. (2020). Finansal performans ile pay senedi getirisi arasındaki ilişkinin bütünleşik CRITIC ve MABAC ÇKKV teknikleriyle ölçülmesi: Borsa İstanbul çimento sektörü firmaları üzerine ampirik bir uygulama. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (40): 471-488.
3. Anderson, C., ve Green, J. (2014). Multi-criteria performance assessment of insurance agencies. Insurance Market Analysis Journal, 22(3), 185-200.
4. Brown, K., ve White, A. (2016). An analytical hierarchy process-based framework for evaluating insurance risks. Risk Analysis, 36(5), 997-1012.
5. Chan, S. T. (2024). The analysis of multi-criteria investment decisions on institutional investors' real estate investment return in Hong Kong. Journal of Real Estate Finance and Economics, 58(3), 567-590. https://doi.org/10.1007/s11448-024-00234-7
6. Diakoulaki, D., Mavrotas, G., ve Papayannakis, L. (1995). Determining Objective Weights in Multiple Criteria Problems: The CRITIC Method. Computers & Operations Research, 22(7): 763-770.
7. Ding, W., ve Xu, Y. (2017). An integrated MCDM approach for insurance product selection. Journal of Financial Intermediation, 36(1), 89-104.
8. Ecer, F., ve Pamucar, D. (2022). A Novel LOPCOW-DOBI Multi-Criteria Sustainability Performance Assessment Methodology: An Application İn Developing Country Banking Sector Omega, 112(2022): 1-17.

9. Ghosh, S., ve Choudhury, P. (2019). An ANP approach for ranking insurance companies in emerging markets. Journal of Emerging Market Finance, 18(1), 1-20.
10. Gigović, Ljubomir- Pamučar, Dragan- Bajić, Zoran - Milićević, Milić (2016). The Combination of Expert Judgment and GIS-MAIRCA Analysis for the Selection of Sites for Ammunition Depots. Sustainability, 8(4): 1-30.
11. Gordon, B., ve Smith, R. (2013). A case study of insurance firm performance using the VIKOR method. Journal of Risk Finance, 14(4), 211-228.
12. Gupta, A., ve Singh, R. (2022). An integrated MCDM approach for assessing insurance service quality. Journal of Financial Services Marketing, 27(4), 309-325.
13. Işık, Ö. (2023). Covıd-19 Salgınının Katılım Bankacılığı Sektörünün Performansına Etkisinin Merec-Psı-Maırca Modeliyle İncelenmesi. Nişantaşı Üniversitesi Sosyal Bilimler Dergisi, 10(2). https://doi.org/10.52122/nisantasisbd.1167829
14. Işık, Ö., ve Adalar, İ. (2025). A multi-criteria sustainability performance assessment based on the extended CRADIS method under intuitionistic fuzzy environment: A case study of Turkish non-life insurers. Neural Computing and Applications, 37(5), 3317-3342. https://doi.org/10.1007/s00521-024-10803-0
15. Johnson, P., ve Lee, H. (2013). The role of decision support systems in insurance company management. Decision Analysis Journal, 20(2), 149-165.
16. Jones, M., ve Baker, T. (2021). Evaluating financial risk in the insurance sector using hybrid MCDM methods. Insurance Mathematics and Economics, 98(4), 145-162.
17. Karadağ, M. M. (2024). Insurance companies' financial performance traded on BIST implementing the CRITIC-based PIV method. Investment Management and Financial Innovations, 21(2), 127-143. https://doi.org/10.21511/imfi.21 (2).2024.11
18. Khabbazi, N. M. P., & Fashkache, H. T. (2024). Comparing performance of insurance companies using artificial intelligence through multi-criteria decision-making methods. International Journal of Sustainable Applied Science, 12(1), 211-229.
19. Kumar, R., ve Patel, D. (2021). Application of VIKOR method in ranking insurance companies. Economic Computation and Economic Cybernetics Studies and Research, 55(1), 123-140.
20. Lee, H., ve Park, S. (2016). The impact of insurance competition on financial stability: An MCDM approach. Journal of Financial Stability, 27, 85-101.
21. Mousavi, S., ve Aghaei, M. (2020). A novel hybrid MCDM model for assessing insurance market stability. Journal of Risk and Insurance, 87(3), 221-240.
22. Pamucar, Dragan S.- Tarle, Snezana Pejcic- Parezanovic, Tanja (2018). New Hybrid MultiCriteria Decision-Making DEMATEL-MAIRCA Model: Sustainable Selection of a Location for the Development of Multimodal Logistics Centre. Economic Research Ekonomska istraživanja, 31(1): 1641-1665.
23. Patel, H., ve Sharma, K. (2019). A comparative study of DEA and TOPSIS in insurance company evaluation. International Journal of Finance & Banking Studies, 8(2), 77-95.
24. Rahman, M., ve Alam, S. (2022). Comparative analysis of MCDM techniques in insurance investment decision-making. Applied Soft Computing, 120, 107394. https://doi.org/10.1016/j.asoc.2022.107394
25. Ramirez, F., ve Castillo, R. (2015). Multi-criteria decision-making in insurance underwriting. Insurance: Mathematics and Economics, 62, 23-38.
26. Sharma, V., ve Bhattacharya, A. (2021). The role of AHP in assessing customer satisfaction in the insurance sector. Service Business Review, 16(3), 245-267.
27. Singh, P., ve Kaur, R. (2019). Evaluating insurance company performance using entropy-based MCDM techniques. International Journal of Financial Studies, 7(4), 67-85.
28. Taşcı, M. Z., ve Akbalık, M. (2022). Türk Sigorta Sektöründe Hayat/Emeklilik Branşında Faaliyet Gösteren Sigorta Şirketlerinin Çok Kriterli Karar Verme Yöntemleri ile Performans Analizi. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 23(3), 726-735.
29. Taylor, J., ve Patel, K. (2014). Decision support for insurance portfolio management using MCDM. Financial Decision Making, 30(1), 34-51.
30. Vogel, S., ve He, R. (2017). Application of DEA and AHP in assessing insurance company efficiency. European Journal of Operational Research, 263(2), 697-709.
31. Yılmaz, B., ve Özkan, Ö. (2022). Financial efficiency evaluation of life insurance firms using DEA and TOPSIS methods. Financial Modelling and Policy Analysis, 14(2), 89-105.
32. Zavadskas, E. K., ve Podvezko, V. (2016). Integrated determination of objective criteria weights in MCDM. International Journal of Information Technology and Decision Making, 15(2), 267-283.
33. Zhang, L., ve Wu, J. (2015). A hybrid fuzzy MCDM approach for evaluating insurance firms. Applied Soft Computing, 35, 526-539.
34. Zhao, L., ve Wang, Q. (2020). Decision support for insurance claim settlement using PROMETHEE. Decision Support Systems, 138, 113429.
35. Zhu, J., ve Chen, L. (2018). Fuzzy logic-based MCDM approach in insurance service evaluation. Journal of Business Research, 92, 173-185.