SavedAdd your journal

Journals / Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Volume: 13 Issue: 2

CAMELS-ESG Risk Performance Assessment in Banking Using a Grey Multi-Criteria Decision-Making Approach

Osman Yavuz Akbulut(SIVAS CUMHURIYET UNIVERSITY)0000-0001-9225-1728
Yusuf Akgül(SIVAS CUMHURIYET UNIVERSITY)0000-0001-7327-3913
Pages
—
Publication Date
2026-06-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results reveal that ESG risk, impaired loans to gross loans, and liquid assets to deposits constitute the most influential factors in shaping banks’ overall multidimensional performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The sustainability of the banking industry increasingly depends on the effective integration of traditional financial soundness indicators with environmental, social, and governance (ESG) risk considerations.
📊
Key Findings & Data:In this research, a novel grey-based group decision-making framework is developed to assess the multidimensional CAMELS–ESG risk performance of banks operating in Turkey under conditions of uncertainty and incomplete information.
💡
Conclusion & Impact:The results reveal that ESG risk, impaired loans to gross loans, and liquid assets to deposits constitute the most influential factors in shaping banks’ overall multidimensional performance.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Banking Sector · CAMELS · ESG Risk

Abstract

The sustainability of the banking industry increasingly depends on the effective integration of traditional financial soundness indicators with environmental, social, and governance (ESG) risk considerations. In this research, a novel grey-based group decision-making framework is developed to assess the multidimensional CAMELS–ESG risk performance of banks operating in Turkey under conditions of uncertainty and incomplete information. The suggested methodology integrates the grey extensions of the Grey Symmetry Point of Criterion (G-SPC) and Grey Preference Selection Index (G-PSI) methods to objectively determine criterion importance weights, which are subsequently aggregated through a newly developed linear weight integration operator. The ranking of bank alternatives is then derived using the Grey Ranking of Alternatives by Criterion Weights (G-RAWEC) approach. The empirical analysis is conducted on eight deposit banks using six CAMELS-based financial indicators and one ESG risk criterion. The results reveal that ESG risk, impaired loans to gross loans, and liquid assets to deposits constitute the most influential factors in shaping banks’ overall multidimensional performance. The ranking outcomes indicate that Akbank achieves the highest performance, followed by Yapı Kredi Bank and Garanti BBVA, whereas Şekerbank, QNB Finansbank, and Vakıfbank exhibit relatively lower performance levels. Extensive robustness and sensitivity analyses-including parameter variation, comparison with alternative grey-based MCDM methods, rank reversal testing, and criterion elimination scenarios-confirm the stability, reliability, and internal consistency of the proposed framework. This study contributes to both the banking performance and MCDM literature. The proposed framework offers a transparent and practically applicable decision-support tool for regulators, bank managers, and investors seeking to assess banking performance in uncertain and complex decision environments.

🔖

Cite This Article

Akbulut, O. Y., Akgül, Y. (2026). CAMELS-ESG Risk Performance Assessment in Banking Using a Grey Multi-Criteria Decision-Making Approach. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Volume: 13 Issue: 2. https://ajindex.com/articles/1149251

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (54)

  1. Andriani, E., Ashsifa, I., & Kuncoro, M. (2024). How geopolitical, credit and financial risks define ASEAN banks’ performance?. Jurnal Ilmiah Manajemen Ekonomi & Akuntansi (Mea), 8(2), 2087-2105. https://doi.org/10.31955/mea.v8i2.4297
  2. Aydın, Y. (2025). A hybrid multi-criteria decision-making model for performance assessment in the banking industry. Knowledge and Decision Systems with Applications, 1, 234-256. https://doi.org/10.59543/kadsa.v1i.14725
  3. Bektaş, S. (2022). Türkiye’deki kamu sermayeli bankalarin sürdürülebilirlik performanslarinin hibrit çkkv model ile değerlendirilmesi: 2014-2021 dönemi MEREC-ARAS modeli örneği. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23(4), 426-442. https://doi.org/10.53443/anadoluibfd.1150277
  4. Betz, F., Oprică, S., Peltonen, T. A., & Sarlin, P. (2014). Predicting distress in European banks. Journal of Banking & Finance, 45, 225-241. https://doi.org/10.1016/j.jbankfin.2013.11.041
  5. Botshekan, M. H., Poor, M. S. A., Takaloo, A., & Naeimabadi, M. (2022). The impact of banking health indicators on return on risk-weighted assets (RORWA) index in commercial banks: evidence from Iran. Journal of Money and Economy, 17(4), 437-456. https://doi.org/10.29252/jme.17.4.437
  6. Bulut, E., & Şimşek, A. İ. (2024). Evaluation of financial performance of BIST participation banks: CAMELS and Multi-Criteria Decision Making (MCDM) approach. Alanya Akademik Bakış, 8(3), 923-940. https://doi.org/10.29023/alanyaakademik.1511040
  7. Chakraborty, S., Raut, R. D., Rofin, T. M., & Chakraborty, S. (2024). An integrated G-MACONT approach for healthcare supplier selection. Grey Systems: Theory and Application, 14(2), 318-336. https://doi.org/10.1108/GS-07-2023-0068
  8. Chaudhuri, T., Mitra, S., Guha, B., Biswas, S., & Kumar, P. (2023). CSR Contributions for environmental sustainability: A comparison of private banks in emerging market. Decision Making: Applications in Management and Engineering, 6(2), 747-771. https://doi.org/10.31181/dmame622023852
  9. Çilek, A., & Şeyranlıoğlu, O. (2024). Do ESG risk ratings affect financial performance? evidence from selected BIST banking sector companies with LODECI and CRADIS methods. In A. Çilek & O. Şeyranlıoğlu (Eds.), Finans Alanında Güncel Uygulamalı Çalışmalar. Özgür Yayınları. https://doi.org/10.58830/ozgur.pub488.c2033
  10. Çıtak, Ö., & Ünlü, U. (2024). BİST’te işlem gören bankaların sürdürülebilirlik raporlarının ÇKKV yöntemi ile değerlendirilmesi. Alanya Akademik Bakış, 8(3), 840-857. https://doi.org/10.29023/alanyaakademik.1474388
  11. Demir, E. (2025). An innovative decision support model for the financial performance assessment: A study of BIST cement firms. Knowledge and Decision Systems with Applications, 1, 125-144. https://doi.org/10.59543/kadsa.v1i.14021
  12. Dong, W. (2024). Analysis of the impact of ESG performance on financial market stability from the perspective of multivariate statistical models. Journal of Electrical Systems, 20(3), 1002-1010. https://doi.org/10.52783/jes.3427
  13. Doumpos, M., & Zopounidis, C. (2010). A multicriteria decision support system for bank rating. Decision Support Systems, 50(1), 55-63. https://doi.org/10.1016/j.dss.2010.07.002
  14. Durmuş, Z. (2025). Assessment of renewable energy performance in Turkey using a novel integrated MSD-CRITIC-RAWEC model. Journal of Operations and Strategic Analysis, 3(1), 49-64. https://doi.org/10.56578/josa030105
  15. Fauzie, S., Pratomo, W., Hasyim, S., & Ruslan, D. (2025). The role of market competition in driving esg integration in the asean banking sector: an empirical analysis. Iop Conference Series Earth and Environmental Science, 1452(1), 012043. https://doi.org/10.1088/1755-1315/1452/1/012043
  16. Gligorić, Z., Gligorić, M., Miljanović, I., Lutovac, S., & Milutinovic, A. (2023). Assessing criteria weights by the symmetry point of criterion (Novel SPC method) – application in the efficiency evaluation of the mineral deposit multi-criteria partitioning algorithm. Computer Modeling in Engineering and Sciences, 136(1), 955–979. https://doi.org/10.32604/cmes.2023.025021
  17. Goel, A., Sahay, N., & Tyagi, A. (2024). Bank performance evaluation of sustainability strategy dimensions in the emerging market using the MCDM approach. Corporate and Business Strategy Review, 5(3), 106-116. https://doi.org/10.22495/cbsrv5i3art10
  18. Görçün, Ö. F., Shabir, M., Çalık, A., & Işık, Ö. (2025). Evaluation of the Financial Performance of the Textile and Apparel Industry in interval type-2 fuzzy environment. Applied Soft Computing, 113830. https://doi.org/10.1016/j.asoc.2025.113830
  19. Infante, J., Curto, T., & Arenas, C. (2025). The impact of fund characteristics on environmental, social, and governance (ESG) risks: An empirical analysis. Asian Business Research Journal, 10(3), 33-44. https://doi.org/10.55220/25766759.305
  20. Işık, Ö., Adalar, İ., & Shabir, M. (2025). Measuring efficiency, productivity and sustainability performance for Islamic banks: a fuzzy expert-based multi-criteria decision support model using spherical fuzzy information. International Journal of Islamic and Middle Eastern Finance and Management. 1753-8394. https://doi.org/10.1108/IMEFM-09-2024-0477
  21. Işık, Ö., Shabir, M., & Moslem, S. (2024). A hybrid MCDM framework for assessing urban competitiveness: A case study of European cities. Socio-Economic Planning Sciences, 96, 102109. https://doi.org/10.1016/j.seps.2024.102109
  22. Işık, Ö., Shabir, M., Demir, G., Puska, A., & Pamucar, D. (2025). A hybrid framework for assessing Pakistani commercial bank performance using multi-criteria decision-making. Financial Innovation, 11(1), 38.https://doi.org/10.1186/s40854-024-00728-x
  23. Işık, Ö., Zolfani, S. H., Shabir, M., & Šaparauskas, J. (2025). A grey-based hybrid decision support framework for assessing the Environmental, Social, and Governance (ESG) sustainable performance: a case study of BIST-listed banks. Technological and Economic Development of Economy, 31(4), 1237-1273. https://doi.org/10.3846/tede.2025.24359
  24. Kim, S. W., & Li, Z. (2021). Understanding the impact of ESG practices in corporate finance. Sustainability, 13(7), 3746. https://doi.org/10.3390/su13073746
  25. Korzeb, Z., & Samaniego-Medina, R. (2019). Sustainability performance: A comparative analysis in the Polish-banking sector. Sustainability, 11(3), 653. https://doi.org/10.3390/su11030653
  26. Krstić, M., Elia, V., Agnusdei, G. P., De Leo, F., Tadić, S., & Miglietta, P. P. (2024). Evaluation of the agri-food supply chain risks: The circular economy context. British Food Journal, 126(1), 113-133. https://doi.org/10.1108/BFJ-12-2022-1116
  27. Liu, J., & Xie, J. (2024). The effect of ESG performance on bank liquidity risk. Sustainability, 16(12), 4927. https://doi.org/10.3390/su16124927
  28. Maghyereh, A. I., & Awartani, B. (2012). Financial integration of GCC banking markets: A non-parametric bootstrap DEA estimation approach. Research in International Business and Finance, 26(2), 181-195. https://doi.org/10.1016/j.ribaf.2011.10.001
  29. Maniya, K., & Bhatt, M. G. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials and Design, 31, 1785–1789. https://doi.org/10.1016/j.matdes.2009.11.020
  30. Marković, V., Stajić, L., Stević, Ž., Mitrović, G., Novarlić, B., & Radojičić, Z. (2020). A novel integrated subjective-objective MCDM model for alternative ranking in order to achieve business excellence and sustainability. Symmetry, 12(1), 164. https://doi.org/10.3390/sym12010164
  31. Marzouki, A., Chouaibi, J., & Amara, T. (2023). Do business ethics moderate corporate corruption risk-esg reporting relationship? evidence from European ESG firms. International Journal of Ethics and Systems, 40(4), 734-758. https://doi.org/10.1108/ijoes-07-2023-0166
  32. Mastilo, Z., Štilić, A., Gligović, D., & Puška, A. (2024). Assessing the banking sector of Bosnia and Herzegovina: An analysis of financial indicators through the MEREC and MARCOS methods. Journal of Central Banking Theory and Practice, 13(1), 167-197. https://doi.org/10.2478/jcbtp-2024-0008
  33. Mirza, N., Umar, M., Lobont, O. R., & Safi, A. (2025). ESG lending, technology investment and banking performance in BRICS: Navigating sustainability and financial stability. China Finance Review International, 15(2), 324-336. https://doi.org/10.1108/CFRI-09-2024-0496
  34. Nahar, J., Hossain, M., Rahman, M., & Hossain, M. (2024). Advanced predictive analytics for comprehensive risk assessment in financial markets: Strategic applications and sector-wide implications. GMJ, 3(4), 39-53. https://doi.org/10.62304/jbedpm.v3i4.148
  35. Onocak, D. (2024). Türkiye’deki mevduat bankalarının entropi ağırlıklandırmalı CAMELS ve ESG performans analizi. Muhasebe ve Denetime Bakış Dergisi, 24(72), 19-42. https://doi.org/10.55322/mdbakis.1290277
  36. Özalıcı, M., & Bumin, M. (2020). An integrated multi-criteria decision making model with Self-Organizing Maps for the assessment of the performance of publicly traded banks in Borsa Istanbul. Applied Soft Computing, 90, 106166. https://doi.org/10.1016/j.asoc.2020.106166
  37. Peci, A., Dervishaj, B., & Puška, A. (2025). Using fuzzy analytic hierarchy process and technique for order of preference by similarity to the ideal solution in performance evaluation in the Albanian banking sector. Journal of Risk and Financial Management, 18(3), 116. https://doi.org/10.3390/jrfm18030116
  38. Puente De La Vega Caceres, A. (2024). Drivers of value creation and the effect of ESG risk rating on investor perceptions through financial metrics. Sustainability, 16(13), 5347. https://doi.org/10.3390/su16135347
  39. Puška, A., Štilić, A., Pamučar, D., Božanić, D., & Nedeljković, M. (2024). Introducing a novel multi-criteria ranking of alternatives with weights of criterion (RAWEC) model. MethodsX, 12, 102628. https://doi.org/10.1016/j.mex.2024.102628
  40. Qayyum, A., & Khan, S. (2022). X-efficiency, scale economies, technological progress, and competition: the banking sector in pakistan. The Pakistan Development Review, 733-748. https://doi.org/10.30541/v45i4iipp.733-748
  41. Quynh, V. T. N. (2023). An integrated dynamic generalized trapezoidal fuzzy AHP TOPSIS approach for evaluating sustainable performance of bank. Advances in Decision Sciences, 27(1), 01-17.
  42. Rakočević, S. B., & Benković, S. (2023). ESG adoption in the banking sector of the Western Balkan Countries. In Proceedings of the 4th international conference Economic and Business Trends Shaping the Future, 76-86. https://doi.org/10.47063/ebtsf.2023.0007
  43. Reig-Mullor, J., & Brotons-Martinez, J. M. (2021). The evaluation performance for commercial banks by intuitionistic fuzzy numbers: The case of Spain. Soft Computing, 25(14), 9061-9075. https://doi.org/10.1007/s00500-021-05847-6
  44. Rooh, S., Zahid, M., Malik, M. F., & Tahir, M. (2021). Corporate governance characteristics and environmental, social & governance (ESG) performance: Evidence from the banking sector of Pakistan. Journal of Business & Tourism, 7(1), 35-50. https://doi.org/10.34260/jbt.v7i1.218
  45. Şahin, Ö., Bax, K., Czado, C., & Paterlini, S. (2022). Environmental, social, governance scores and the missing pillar—why does missing information matter? Corporate Social Responsibility and Environmental Management, 29(5), 1782-1798. https://doi.org/10.1002/csr.2326
  46. Tommaso, C., & Thornton, J. (2020). Do ESG scores effect bank risk taking and value? evidence from european banks. Corporate Social Responsibility and Environmental Management, 27(5), 2286-2298. https://doi.org/10.1002/csr.1964
  47. Torkayesh, A. E., Zolfani, S. H., Kahvand, M., & Khazaelpour, P. (2021). Landfill location selection for healthcare waste of urban areas using hybrid BWM-grey MARCOS model based on GIS. Sustainable Cities and Society, 67, 102712. https://doi.org/10.1016/j.scs.2021.102712
  48. Ulutaş, A., Balo, F., Sua, L., Demir, E., Topal, A., & Jakovljević, V. (2021). A new integrated grey MCDM model: Case of warehouse location selection. Facta Universitatis, Series: Mechanical Engineering, 19(3), 515-535. https://doi.org/10.22190/FUME210424060U
  49. Ulutaş, A., Topa, A., Karabasevic, D., Zavadskas, E. K., Demirbaş, M., & Üre, S. (2025). A Novel Grey MCDM Model Assessing Macroeconomic Performance of G7 Countries. Journal of Grey System, 37(3). 61-72.
  50. Wanke, P., Kalam Azad, M. A., Barros, C. P., & Hadi‐Vencheh, A. (2016). Predicting performance in ASEAN banks: An integrated fuzzy MCDM–neural network approach. Expert Systems, 33(3), 213-229. https://doi.org/10.1111/exsy.12144
  51. Yazo-Cabuya, E., Ibeas, A., & Herrera, J. (2024). Integrating sustainability into risk management through analytical network process. Sustainability, 16(6), 2384. https://doi.org/10.3390/su16062384
  52. Yeboah, A. (2020). An estimation of the impact of financial intermediation on the economic growth of Ghana; with emphasis on the private sector. European Journal of Business and Management Research, 5(5), 1-12. https://doi.org/10.24018/ejbmr.2020.5.5.529
  53. Zavadskas, E. K., Kaklauskas, A., Turskis, Z., & Tamošaitiene, J. (2008). Selection of the effective dwelling house walls by applying attributes values determined at intervals. Journal of civil engineering and management, 14(2), 85-93. https://doi.org/10.3846/1392-3730.2008.14.3
  54. Zhao, H., Sinha, A. P., & Ge, W. (2009). Effects of feature construction on classification performance: An empirical study in bank failure prediction. Expert Systems with Applications, 36(2), 2633-2644. https://doi.org/10.1016/j.eswa.2008.01.053