SavedAdd your journal

Journals / Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Cilt: 28 Sayı: Özel Sayı Özel Sayı

Integrated LOPCOW–RAFSI Approach for Banking Performance Assessment

Bankacılık Performans Değerlendirmesinde Entegre LOPCOW–RAFSI Yaklaşımı

Fatih Akdeniz(GÜMÜŞHANE ÜNİVERSİTESİ, GÜMÜŞHANE İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ)0000-0003-0518-0242
Birol Güven(GÜMÜŞHANE ÜNİVERSİTESİ, GÜMÜŞHANE İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ)0000-0002-7077-9238
Pages
169–181
Publication Date
2026-10-21
🏛️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)

On the other hand, VAKBN and HALKB generally ranked lower, which may reflect the responsibilities of state-owned banks regarding public policies, in part.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study evaluates the financial performance of seven banks in the BIST Banking Index using the LOPCOW and RAFSI multi-criteria decision-making methods.
📊
Key Findings & Data:The study covers annual financial data for 2022–2024.
💡
Conclusion & Impact:On the other hand, VAKBN and HALKB generally ranked lower, which may reflect the responsibilities of state-owned banks regarding public policies, in part.
🔬Design:Araştırma Makalesi
📍Scope:2022–2024 Verileri
⚙️Variables:Finansal Performans · ÇKKV · LOPCOW-RAFSI

Abstract

This study evaluates the financial performance of seven banks in the BIST Banking Index using the LOPCOW and RAFSI multi-criteria decision-making methods. The study covers annual financial data for 2022–2024. It uses 13 financial ratios, including capital adequacy, liquidity, profitability, asset quality, income-expense structure, operating efficiency, and market-based performance indicators. In this context, the study determined criterion weights using the LOPCOW method and ranked banks by financial performance using the RAFSI method. The analysis findings show that the criterion weights changed significantly over the years. This stems from the banking sector's dynamic nature and changes in financial indicators driven by varying macroeconomic conditions. The results reveal that GARAN exhibited the highest performance during the relevant period, while QNBTR showed an upward performance trend. On the other hand, VAKBN and HALKB generally ranked lower, which may reflect the responsibilities of state-owned banks regarding public policies, in part.

Özet

Bu çalışmanın amacı, çok kriterli karar verme yöntemlerinden olan LOPCOW ve RAFSI kullanılarak BIST Bankacılık Endeksi'nde faaliyet gösteren yedi bankanın finansal performansının değerlendirmektir. Çalışmanın kapsamı 2022–2024 dönemine ait yıllık finansal verileri kapsamaktadır. Çalışmada sermaye yeterliliği, likidite, kârlılık, varlık kalitesi, gelir–gider yapısı, faaliyet verimliliği ve piyasa temelli performans göstergelerini içeren 13 finansal oran kullanılmaktadır. Bu bağlamda LOPCOW yöntemi ile değişkenlere ilişkin kriter ağırlıkları belirlenmiş, RAFSI yöntemi ile ise bankaların finansal performanslarına göre sıralaması yapılmıştır. Analiz bulguları, kriter ağırlıklarının yıllar itibarıyla önemli ölçüde değiştiğini göstermektedir. Söz konusu durum, bankacılık sektörünün dinamik yapısı ile farklılaşan makroekonomik koşullara bağlı olarak finansal göstergelerin değişiminden kaynaklanmaktadır. Sonuçlar, GARAN'ın ilgili dönem boyunca en yüksek performansı sergilediğini, QNBTR'nin ise yükselen bir performans eğilimi içinde olduğunu ortaya koymaktadır. Öte yandan VAKBN ve HALKB genellikle alt sıralarda yer almış olup bu durumun kısmen kamu bankalarının kamu politikalarına ilişkin sorumluluklarını yansıttığı düşünülmektedir.

🔖

Cite This Article

Akdeniz, F., Güven, B. (2026). Integrated LOPCOW–RAFSI Approach for Banking Performance Assessment. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 28 Sayı: Özel Sayı Özel Sayı, 169–181. https://doi.org/10.33707/akuiibfd.1965122

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (64)

  1. Abdel-Basset, M., Mohamed, R., Elhoseny, M., Abouhawash, M., Nam, Y., & AbdelAziz, N. M. (2021). Efficient MCDM model for evaluating the performance of commercial banks: A case study. Computers, Materials and Continua, 67(3), 2729-2746.
  2. Akbulut, O. Y. (2019). CRITIC ve EDAS Yöntemleri ile İş Bankası’nın 2009-2018 Yılları Arasındaki Performansının Analizi. Ekonomi Politika ve Finans Araştırmaları Dergisi, 4(2), 249-263. https://doi.org/10.30784/epfad.594762
  3. Akbulut, O. Y., & Aydın, Y. (2024). A Hybrid Multidimensional Performance Measurement Model Using the MSD- MPSI-RAWEC Model for Turkish Banks. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 11(3), 1157-1183. https://doi.org/10.30798/makuiibf.1464469
  4. Akkaya, C., & Avşarlıgil, N. (2025). Bankaların Finansal Performanslarının Değerlendirilmesi: Türkiye Bankacılık Sektöründe Bir İnceleme. Alanya Akademik Bakış, 9(1), 290-307. https://doi.org/10.29023/alanyaakademik.1567547
  5. Aksoy, T., & Güzel, A. (2023). Banking in Türkiye: History, Evaluation, and Role in the Financial System. In History of Accounting, Management, Business and Economics, Volume I (pp. 103-144). Singapore: Springer Nature Singapore.
  6. Ali, J., Hussain, K. N., Alnoor, A., Muhsen, Y. R., & Atiyah, A. G. (2024). Benchmarking methodology of banks based on financial sustainability using CRITIC and RAFSI techniques. Decision Making: Applications in Management and Engineering, 7(1), 315-341. https://doi.org/10.31181/dmame712024945
  7. Alkahtani, B. S. T., Simic, V., Anjum, M., Pamucar, D., Algahtani, O., & Alzaid, S. S. (2025). Decision Support System for Financial and Accounting Performance Assessment in Manufacturing Industries. International Journal of Computational Intelligence Systems, 18(1), 314. https://doi.org/10.1007/s44196-025-01066-1
  8. Altıntaş, F. F. (2024). Analysis of Artificial Intelligence Readiness Performances of G7 Countries: An Application with LOPCOW-based MARCOS Method. Computer Science, 9(Issue: 2), 99-121. https://doi.org/10.53070/bbd.1537792
  9. Avlaç, S., & Demirel, E. (2026). Entropi Tabanli Moora Yöntemi İle Borsa İstanbul Banka Endeksinde Bulunan Şirketlerin Finansal Performans Analizi. Parion Akademik Bakış Dergisi, 5(1), 67-77. https://izlik.org/JA97ZR58HZ
  10. Ayaydın, H. (2025). Katılım Bankalarının Finansal Performanslarının Değerlendirilmesi: Gri İlişkisel Analiz Yöntemi. The Journal of International Scientific Researches, 10(2), 265-275. https://doi.org/10.23834/isrjournal.1638416
  11. Ayaydın, H., Çam, A. V., Pala, F., & Sarı, Ş. (2018). Türk Bankacılık Sektöründe Performans Değerlendirmesi: AHS ve TOPSIS Yöntemleri Uygulaması. Global Journal of Economıcs and Busıness Studıes, 7(13), 51-64.
  12. Ayaydın, H., Shırzadı, S. A., Balıkçı, H. C., Sharabati, A. A. A., & Barut, A. (2026). Entropy-based VIKOR and TOPSIS approaches for financial performance ranking: evidence from Turkish cement companies listed on Borsa Istanbul. Quality & Quantity, 1-23. https://doi.org/10.1007/s11135-025-02494-z
  13. Aydin, 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
  14. Bayram, E. (2021). Türkiye’deki Katılım Bankalarının Critic Temelli Edas Yöntemiyle Performans Değerlendirmesi. Finansal Araştırmalar ve Çalışmalar Dergisi, 13(24), 55-72.
  15. BDDK, (2025). Türk Bankacılık Sektörü Temel Göstergeleri, Aralık 2025. https://www.bddk.org.tr/Veri/EkGetir/8? ekId=590
  16. Beheshtinia, M. A., & Omidi, S. (2017). A hybrid MCDM approach for performance evaluation in the banking industry. Kybernetes, 46(8), 1386-1407.
  17. Berger, A. N., & Boot, A. W. (2024). Financial intermediation services and competition analyses: Review and paths forward for improvement. Journal of Financial Intermediation, 57, 101072. https://doi.org/10.1016/j.jfi.2024.101072
  18. Bueno, L. A., Sigahi, T. F., Rampasso, I. S., Leal Filho, W., & Anholon, R. (2024). Impacts of digitization on operational efficiency in the banking sector: Thematic analysis and research agenda proposal. International Journal of Information Management Data Insights, 4(1), 100230.
  19. Çakalı, K. R. (2022). Performance evaluation of deposit banks with financial ratios: combined use of objective and subjective criteria weighting methods (Combined Entropy-SWARA Based EDAS Method). Alanya Akademik Bakış, 6(2), 2351-2377.
  20. Çanakçıoğlu, M., & Küçükönder, H. (2026). Türkiye’de faaliyet gösteren mevduat bankalarının finansal performanslarının Entropi, Cradis ve Copeland yöntemleri ile değerlendirilmesi. Afyon Kocatepe Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 28(1), 220-238. https://doi.org/10.33707/akuiibfd.1835293
  21. Chinh, D. T., Hien, N. T. T., Quynh, P. H., & Minh, V. Q. (2025). Comparing Methodologies for Ranking Alternatives: A case study in assessing bank financial performance. arXiv preprint arXiv:2509.08451.
  22. 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
  23. Demir, G. (2021). Özel sermayeli mevduat bankalarında performans analizi: SWARA-RAFSI bütünleşik model uygulaması. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 35(4), 1359-1382.
  24. Demir, G. (2022). Analysis of the financial performance of the deposit banking sector in the Covid-19 period with LMAW-DNMA methods. International Journal of Insurance and Finance, 2(2), 17-36.
  25. Deniz, G. (2026). Türkiye’de Kurulan Yabancı Sermayeli Mevduat Bankalarının Finansal Performans Analizi: CRITIC ve MARCOS Metodu. İktisadi İdari Ve Siyasal Araştırmalar Dergisi, 29, 70-89. https://doi.org/10.25204/iktisad.1738463
  26. Dey, S., Mitra, G., Biswas, S., & Pamucar, D. (2025). Evaluation of fim performance under merger and acquisition effect: An integrated lopcow-piv approach. Decision Making: Applications in Management and Engineering, 8(1), 588-614. https://doi.org/10.31181/dmame8120251448
  27. Durdu, D. (2025). Evaluating financial performance with SPC-LOPCOW-MARCOS hybrid methodology: a case study for firms listed in BIST sustainability index. Knowledge and Decision Systems with Applications, 1, 92-111. http://www.doi.org/10.59543/kadsa.v1i.13879
  28. Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690
  29. Erdoğan, B. (2022). BIST’e Kayıtlı Bankaların Finansal Performansının AHP-SD Tabanlı PIV Yöntemiyle Değerlendirilmesi. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 52, 93-109. https://doi.org/10.30794/pausbed.1059473
  30. Erdoğan, H. H. (2022). A Multıcrıterıa Decısıon Framework For Bank Performance Evaluatıon in TURKEY. Süleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 27(1), 98.
  31. Ezer, F. (2022). Performance Evaluation of Different Types of Banks in Turkey Using Fuzzy EDAS Method. Journal of Optimization and Decision Making, 1(1), 42-62.
  32. Irvanizam, I., Nasution, M. K., Tulus, T., & Nababan, E. B. (2025). A hybrid decision support framework using MEREC-RAFSI with spherical fuzzy numbers for selecting banking financial aid recipients. IEEE Access, 13, 33101-33123. https://doi.org/10.1109/ACCESS.2025.3539200
  33. Işık, Ö., Adalar, İ., & Shabir, M. (2025). Measuring efficiency, productivity and sustainability performance for ıslamic banks: a fuzzy expert-based multi-criteria decision support model using spherical fuzzy information. International Journal of Islamic and Middle Eastern Finance and Management, 18(6), 1482-1519. http://dx.doi.org/10.1108/IMEFM-09-2024-0477
  34. İyikesici, B. B. & Mete, S. (2025). RAFSI Yöntemi Ile Karbon Ayak Izi Hesaplama Alternatiflerinin Sıralaması. İçinde M. Kabak & C. Gencer (Eds.) Çok Kriterli Karar Vermede Güncel Yaklaşımlar Ve Toplulaştırma Teknikleri. Nobel.
  35. Kadooğlu Aydın, G., Hazar, A., Babuşcu, Ş. ve Uçar, D. (2023). Bankaların multi-moora yöntemi ile risk bazlı performans ölçümü – Türkiye uygulaması. Doğuş Üniversitesi Dergisi, 24(2), 171-192. https://doi.org/10.31671/doujournal.1216012.
  36. Kartal, M. T. (2018). Bankaların finans sektöründeki önemi (Importance of Banks in Finance Sector). Finansal İktisat (Financial Economics), 5-27.
  37. Kavas, Y. B., & Medetoğlu, B. (2024). Financial Performance Measurement with MAIRCA Method: Application on Turkish Banking Sector. Maliye ve Finans Yazıları, 122, 44-58. https://doi.org/10.33203/mfy.1423653
  38. Kırbaşlı, M. (2025). Türk bankacılık sektöründe finansal performans analizi: Kamu ve özel mevduat bankaları üzerine TOPSIS tabanlı bir inceleme (2018-2023). (2025). Business & Management Studies: An International Journal, 13(4), 2101-2119. https://doi.org/10.15295/bmij.v13i4.2674
  39. Kumar, K., & Prakash, A. (2019). Developing a framework for assessing sustainable banking performance of the Indian banking sector. Social Responsibility Journal, 15(5), 689-709. https://doi.org/10.1108/SRJ-07-2018- 0162
  40. Kumari, A. & Devi, N.C. (2022). The Impact of FinTech and Blockchain Technologies on Banking and Financial Services. Technology Innovation Management Review, 12 (1/2): 22010204. http://doi.org/10.22215/timreview/1481
  41. Liu, Z., Zhang, D., & Liu, Q. (2026). An Enhanced Multi-Criteria Decision-Making Framework for Evaluating LLM- Integrated Smart Product-Service Systems Using Spherical Fuzzy Rough Numbers. Applied Soft Computing, 114797. https://doi.org/10.1016/j.asoc.2026.114797
  42. Majidah, M., Rahadian, D., Firli, A., Kusairi, S., Eti, S., Yüksel, S., & Dinçer, H. (2025). Computational Intelligence for Sustainable Banking: A Novel Fermatean Fuzzy LOPCOW–EDAS Framework. International Journal of Advanced Computer Science & Applications, 16(12), 747-753.
  43. Mir, A. A., & Bhat, A. A. (2022). Green banking and sustainability–a review. Arab Gulf Journal of Scientific Research, 40(3), 247-263. https://doi.org/10.1108/AGJSR-04-2022-0017
  44. Murrar, A., Asfour, B., & Paz, V. (2024). Banking sector and economic growth in the digital transformation era: insights from maximum likelihood and Bayesian structural equation modeling. Asian Journal of Economics and Banking, 8(3), 335-353. https://doi.org/10.1108/AJEB-12-2023-0122
  45. No, R. K. G., Niroomand, S., Didehkhani, H., & Mahmoodirad, A. (2021). Modified interval EDAS approach for the multi-criteria ranking problem in banking sector of Iran. Journal of Ambient Intelligence and Humanized Computing, 12(7), 8129-8148. https://doi.org/10.1007/s12652-020-02550-6
  46. Özbek, A. & Aydın, Ş. N. (2025). Çok Kriterli Karar Verme Yöntemleri Ve Excel Ile Problem Çözümü: Kavram – Teori – Uygulama (4. Baskı). Seçkin Yayıncılık.
  47. Özbek, A., & Aydın, Ş. N. (2025). Türkiye’de Kurulan Yabancı Sermayeli Bankaların Entropi ve ARAS Yöntemleri ile Analizi. The International New Issues in Social Sciences, 13(1), 47-68. https://izlik.org/JA25WH39SP
  48. Rodrigues, J. F., Ferreira, F. A., Pereira, L. F., Carayannis, E. G., & Ferreira, J. J. (2020). Banking digitalization:(Re) thinking strategies and trends using problem structuring methods. IEEE Transactions on Engineering Management, 69(4), 1517-1531.
  49. Roy, P., & Shaw, K. (2023). A fuzzy MCDM decision-making model for m-banking evaluations: comparing several m-banking applications. Journal of Ambient Intelligence and Humanized Computing, 14(9), 11873- 11895.
  50. Sama, H.R., Kosuri, S.V.K., & Kalvakolanu, S. (2022). Evaluating and ranking the Indian private sector banks A multi-criteria decision-making approach. Journal of Public Affairs, 22(2). https://doi.org/10.1002/pa.2419
  51. Shaw, V. K., Biswas, S., Bandyopadhyay, G., & Chatterjee, P. (2025). Evaluating stock performance and governance in commercial banks: a decision analytics perspective. International Journal of Indian Culture and Business Management, 36(2), 243-284. https://doi.org/10.1504/IJICBM.2025.148925
  52. Şimşek, A. İ., Gür, Y. E., & Ünal, E. (2025). Innovative MCDM-ML algorithms-based decision-support system for electric vehicle selection. Environment, Development and Sustainability, 1-26. https://doi.org/10.1007/s10668- 025-06476-x
  53. Sümerli Sarigül, S., Avci, P. & Yaşar, E. (2023). Evaluation of the Financial Performance of Deposit Banks Operating in Turkey by Entropy-supported EDAS Method. Journal of Emerging Economies and Policy, 8(1), 239-255.
  54. Süzülmüş, S., & Yakut, E. (2024). Critic Temelli Promethee ve Edas Teknikleriyle Bankaların Finansal Performanslarının Belirlenerek Karşılaştırılması. MANAS Sosyal Araştırmalar Dergisi, 13(1), 218-239. https://doi.org/10.33206/mjss.1267073
  55. Torkan, S., Mahmoodirad, A., Niroomand, S., & Ghane, S. (2024). Interval promethee ii, topsis and edas approaches for multi-criteria ranking problem of the bank branches in iran. Fuzzy Optimization and Modeling Journal (FOMJ), 5(2), 1-18. https://doi.org/10.71808/fomj.2024.1002637
  56. Uslu, H. (2022). The role of intellectual capital in financial development: evidence from the banking sector of Turkey. Competitiveness Review: An International Business Journal, 32(2), 230-249. http://dx.doi.org/10.1108/CR-06-2020-0084
  57. Wang, J., Setiawansyah, S., & Sumanto, S. (2026). Comparison of Objective Weighting Methods in SAW and Their Effect on Alternative Ranking Results. Jurnal Masyarakat Informatika, 17(1), 89-112. https://doi.org/10.14710/jmasif.17.1.78414
  58. Wang, W., Chen, Y., Wang, Y., Deveci, M., Al-Hinai, A., & Kadry, S. (2025). Analyzing the barriers to blockchain adoption in supply chain finance using an integrated interval-valued Fermatean fuzzy RAFSI model. Financial Innovation, 11(1), 9. https://doi.org/10.1186/s40854-024-00670-y
  59. Wang, Y., Liu, C., & Deng, C. (2023). An ExpTODIM-EDAS-based multiple attribute group decision-making framework for supervision effectiveness evaluation of small and medium sized financial institutions in rural areas under intuitionistic fuzzy circumstances. Ieee Access, 12, 147694-147708. https://doi.org/10.1109/ACCESS.2023.3333432
  60. 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.
  61. Wu, H. Y., Tzeng, G. H., & Chen, Y. H. (2009). A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard. Expert systems with applications, 36(6), 10135-10147. https://doi.org/10.1016/j.eswa.2009.01.005
  62. Yavuz, İ. (2025). Finansal Olmayan Performansın LOPCOW ve ARAS Yöntemleriyle Analizi: 2022-2023 Dönemi İçin Özel Sermayeli Mevduat Bankaları Üzerine Bir Araştırma. Toplum Ekonomi ve Yönetim Dergisi, 6(1), 44-62. https://doi.org/10.58702/teyd.1601069
  63. Yılmaz, N., & Civelek, M. (2025). Küresel Krizler Bağlamında Mevduat Bankacılığı Sektöründe Finansal Performansın LOPCOW-RAFSI ÇKKV Yöntemleri ile Değerlendirilmesi: Kırgızistan Örneği. Ekonomi Politika ve Finans Araştırmaları Dergisi, 10(3), 1191-1217.
  64. Žižović, M., Pamučar, D., Albijanić, M., Chatterjee, P., & Pribićević, I. (2020). Eliminating rank reversal problem using a new multi-attribute model—The RAFSI method. Mathematics, 8(6), 1015. https://doi.org/10.3390/math8061015