Journals / Iğdır Üniversitesi Sosyal Bilimler Dergisi / 2026 / Sayı: 43

Digitalization, Operational Capacity, and Credit Risk: Provincial-Level NPL Dynamics in Türkiye

Dijitalleşme, Operasyonel Kapasite ve Kredi Riski: Türkiye’de İl Düzeyinde NPL Belirleyicileri

Murat Eren(IĞDIR ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ)0000-0002-3855-5429
Serkan Künü(IĞDIR ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ)0000-0002-8641-5850
Ebru Yavuz(IĞDIR ÜNİVERSİTESİ, SOSYAL BİLİMLER ENSTİTÜSÜ)0009-0006-6725-9692
Pages
112–128
Publication Date
2026-10-15
🏛️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 empirical results indicate that the level of digitalization, POS and ATM density, and the number of personnel positively affect the ratio of non-performing loans, whereas the variable representing the COVID-19 period exerts a negative effect.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines the provincial-level dynamics of non-performing loans in Türkiye’s banking system within the framework of banks’ digitalization strategies, operational capacity choices, and liquidity management practices, using an annual dataset covering 81 provinces for the period 2011–2024.
📊
Key Findings & Data:In addition, to account for conditions that may have affected credit markets in an extraordinary manner, variables capturing the credit risk implications of the COVID-19 period and the 2023 earthquakes were incorporated into the model.
💡
Conclusion & Impact:The empirical results indicate that the level of digitalization, POS and ATM density, and the number of personnel positively affect the ratio of non-performing loans, whereas the variable representing the COVID-19 period exerts a negative effect.
🔬Design:Modeling & Simulation
📍Scope:2011–2024 Verileri
⚙️Variables:Dijitalleşme · Operasyonel Kapasite · Takipteki Krediler

Abstract

This study examines the provincial-level dynamics of non-performing loans in Türkiye’s banking system within the framework of banks’ digitalization strategies, operational capacity choices, and liquidity management practices, using an annual dataset covering 81 provinces for the period 2011–2024. The analysis investigates the relationship between non-performing loans and several indicators, including the level of digitalization, POS and ATM density, the number of branches and personnel, and the loan-to-deposit ratio. In addition, to account for conditions that may have affected credit markets in an extraordinary manner, variables capturing the credit risk implications of the COVID-19 period and the 2023 earthquakes were incorporated into the model. The estimations were conducted under a fixed-effects specification using the Driscoll–Kraay standard errors method, which accounts for regional heterogeneity and temporal dependence. The empirical results indicate that the level of digitalization, POS and ATM density, and the number of personnel positively affect the ratio of non-performing loans, whereas the variable representing the COVID-19 period exerts a negative effect. By contrast, no statistically significant relationship is identified for branch density, the loan-to-deposit ratio, or the variable representing provinces affected by the earthquakes. The findings suggest that increases in digitalization and operational capacity may accelerate credit expansion and thereby contribute to risk accumulation. In this context, the need to consider the efficiency gains generated by digitalization together with credit growth and the tightening of credit evaluation processes comes to the fore. Furthermore, the results highlight the importance of adopting more balanced risk management approaches that account for regional heterogeneity, which is critical for maintaining financial stability. With these findings, the study offers an original contribution to the literature by jointly modeling digitalization indicators with the physical infrastructure of banking at the provincial level.

Özet

Türkiye’de bankacılık sisteminin il düzeyinde sergilediği takipteki kredi dinamiklerini, banka yönetimlerinin dijitalleşme stratejileri, operasyonel kapasite tercihleri ve likidite yönetimi uygulamaları çerçevesinde inceleyen bu çalışmada 81 il için 2011–2024 dönemi yıllık veri seti kullanılmıştır. Çalışmada dijitalleşme düzeyi, POS ve ATM yoğunluğu, şube ve personel sayısı ile kredi/mevduat oranının takipteki kredilerle ilişkisi analiz edilmiştir. Ayrıca, kredi piyasasını olağan dışı biçimde etkileyebilecek koşulları dikkate almak amacıyla COVID-19 dönemi ve 2023 depremlerinin kredi riskine yansımalarını kontrol eden değişkenler modele dâhil edilmiştir. Tahminler bölgesel heterojen unsurlar ve zamansal bağımlılıklar dikkate alınarak sabit etkiler modeli altında Driscoll–Kraay standart hatalar yöntemi kullanılarak gerçekleştirilmiştir. Ampirik analiz sonucunda, dijitalleşme düzeyinin, POS ve ATM yoğunluğunun ve personel sayısının takipteki kredi oranlarını pozitif yönde; COVID-19 dönemini temsil eden değişkenin ise negatif yönde etkilediği sonucuna ulaşılmıştır. Buna karşılık, şube yoğunluğu, kredi/mevduat oranı ve depremden etkilenen illeri temsil eden değişken için istatistiksel olarak anlamlı bir ilişki tespit edilmemiştir. Elde edilen bulgular, dijitalleşme ve operasyonel kapasite artışının kredi genişlemesini hızlandırarak risk birikimini artırabileceğini göstermektedir. Bu çerçevede, dijitalleşmenin sağladığı verimlilik kazanımlarının kredi büyümesi ve kredi değerlendirme süreçlerindeki sıkılaşma ile birlikte ele alınması gerekliliği öne çıkmaktadır. Ayrıca bölgesel heterojenliği dikkate alan daha dengeli risk yönetimi yaklaşımlarının finansal istikrar açısından kritik olduğu anlaşılmaktadır. Bu bulgularla çalışma, dijitalleşme göstergelerini bankacılığın fiziksel altyapı unsurlarıyla birlikte il düzeyinde modelleyerek literatüre özgün bir katkı sunmaktadır.

🔖

Cite This Article

Eren, M., Künü, S., Yavuz, E. (2026). Digitalization, Operational Capacity, and Credit Risk: Provincial-Level NPL Dynamics in Türkiye. Iğdır Üniversitesi Sosyal Bilimler Dergisi, Sayı: 43, 112–128. https://doi.org/10.54600/igdirsosbilder.1873487

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (28)

  1. Abdioğlu, N., & Aytekin, S. (2016). Takipteki kredi oranını etkileyen faktörlerin belirlenmesi: Mevduat bankaları üzerinde bir dinamik panel veri uygulaması. İşletme Araştırmaları Dergisi, 8(1), 538–555.
  2. Aldasoro, I., Fender, I., Hardy, B., & Tarashev, N. (2020). Effects of Covid-19 on the banking sector: The market’s assessment (BIS Bulletin No. 12). Bank for International Settlements. https://www.bis.org/publ/bisbull12.htm
  3. Allahverdi, M. Türkiye’nin Finansal Coğrafyası: Finansal Derinlik-Erişim-Risk/Kırılganlık Ekseninde İl Düzeyi Bölgesel Ayrışma (2007-2025). BDDK Bankacılık ve Finansal Piyasalar Dergisi, 20(1), 23-49.
  4. Bakır, M. R., Çetin, M. A., & Bakırtaş, İ. (2025). Revisiting the macroeconomic determinants of non-performing loans with a deep learning technique with causal inference: Evidence from Türkiye. Borsa Istanbul Review, 25(3), 541-551.
  5. Bankacılık Düzenleme ve Denetleme Kurumu (BDDK). (2025). FinTürk veri tabanı. https://www.bddk.org.tr/BultenFinTurk (Erişim tarihi: 04 Ekim 2025)
  6. Beck, T., Demirgüç-Kunt, A., & Levine, R. (2007). Finance, inequality and the poor. Journal of Economic Growth, 12(1), 27–49. https://doi.org/10.1007/s10887-007-9010-6
  7. Beck, T., Demirgüç-Kunt, A., & Martinez Peria, M. S. (2007). Reaching out: Access to and use of banking services across countries. Journal of Financial Economics, 85(1), 234–266. https://doi.org/10.1016/j.jfineco.2006.07.002
  8. Berger, A. N., & DeYoung, R. (1997). Problem loans and cost efficiency in commercial banks. Journal of banking & finance, 21(6), 849-870.
  9. Bilgi Teknolojileri ve İletişim Kurumu (BTK). (2025). Yıllık il istatistikleri veri tabanı. https://www.btk.gov.tr/yillik-il-istatistikleri (Erişim tarihi: 27 Eylül 2025)
  10. Bongomin, G. O. C., Ntayi, J. M., Munene, J. C., & Malinga, C. A. (2018). Mobile money and financial inclusion in sub-Saharan Africa: The moderating role of social networks. Journal of African Business, 19(3), 361–384. https://doi.org/10.1080/15228916.2017.1416214
  11. Chen, X., Zhao, X., & Chang, C.-P. (2023). The shocks of natural disasters on non-performing loans: Global evidence. Economic Systems, 47(1), 101077. https://ideas.repec.org/a/eee/ecosys/v47y2023i1s0939362522001121.html
  12. Cornelli, G., Frost, J., Gambacorta, L., Rau, R., Wardrop, R., & Ziegler, T. (2020). Fintech and big tech credit: A new database (BIS Working Paper No. 887). Bank for International Settlements. https://www.bis.org/publ/work887.htm
  13. Demirgüç-Kunt, A., Klapper, L., Singer, D., & Ansar, S. (2022). The Global Findex Database 2021: Financial inclusion, digital payments, and resilience in the age of COVID-19. World Bank. https://openknowledge.worldbank.org/handle/10986/37578
  14. Demirgüç-Kunt, A., Pedraza, A., & Ruiz-Ortega, C. (2021). Banking sector performance during the COVID-19 crisis. Journal of Banking & Finance, 133, 106303. https://ideas.repec.org/a/eee/jbfina/v133y2021ics0378426621002570.html
  15. Driscoll, J. C., & Kraay, A. C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 80(4), 549–560. https://doi.org/10.1162/003465398557825
  16. Foos, D., Norden, L., & Weber, M. (2010). Loan growth and riskiness of banks. Journal of Banking & Finance, 34(12), 2929–2940. https://doi.org/10.1016/j.jbankfin.2010.06.007
  17. Işık, O., & Bolat, S. (2016). Determinants of non-performing loans of deposit banks in Turkey. Journal of Business, Economics and Finance, 5(4), 341–350.
  18. Klein, N. (2013). Non-performing loans in CESEE: Determinants and impact on macroeconomic performance. International Monetary Fund.
  19. Louzis, D. P., Vouldis, A. T., & Metaxas, V. L. (2012). Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios. Journal of banking & finance, 36(4), 1012-1027.
  20. Makri, V., Tsagkanos, A., & Bellas, A. (2014). Determinants of non-performing loans: The case of Eurozone. Panoeconomicus, 61(2), 193–206. https://doi.org/10.2298/PAN1402193M
  21. Ozili, P. K. (2018). Impact of digital finance on financial inclusion and stability. Borsa Istanbul Review, 18(4), 329–340. https://doi.org/10.1016/j.bir.2017.12.003
  22. Popov, A., & Udell, G. F. (2012). Cross-border banking, credit access, and the financial crisis. Journal of International Economics, 87(1), 147–161.
  23. Takmaz, S., Sarı, E., & Alataş, S. (2024). Financial inclusion in Turkey: unpacking the provincial inequality and its determinants. Applied Economics Letters, 31(7), 661-669.
  24. Türkiye Bankalar Birliği (TBB). (2025). TBB veri sistemi: İller–bölgeler. https://verisistemi.tbb.org.tr/index.php?/tbb/report_bolgeler (Erişim tarihi: 28 Eylül 2025)
  25. Vardar, G., & Özgüler, İ. C. (2015). Short term and long term linkages among nonperforming loans, macroeconomic and bank-specific factors: An empirical analysis for Turkey. Ege Academic Review, 15(3), 313–326.
  26. Yang, F., & Masron, T. A. (2024). Role of financial inclusion and digital transformation on bank credit risk. Journal of International Financial Markets, Institutions and Money, 91, 101934.
  27. Yarba, I., Er, T. A., & Şengül, A. (2024). Rapid credit expansion and firm behavior: A case study from Türkiye. Borsa Istanbul Review, 24(6), 1137-1145.
  28. Yue, P., Korkmaz, A. G., Yin, Z., & Zhou, H. (2022). The rise of digital finance: Financial inclusion or debt trap? International Review of Economics & Finance, 77, 125–139.