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Dijital bankacılık ve enflasyon ilişkisinin makine öğrenme algoritmaları ile tespiti

Year 2025, Issue: 70, 193 - 199, 30.04.2025

Ayşe Akboz Caner , Ayşe Ergin Ünal , Serkan Nas

https://doi.org/10.18070/erciyesiibd.1618053

Abstract

Son dönemlerde özellikle finans alanında dijital teknolojinin artan kullanımı, pek çok farklı ekonomik göstergenin seyrini değiştirmektedir. Bu dijital teknolojiler, finansal işlemlerde hızlı ve yenilikçi çözümler sunarken, ekonominin dinamiklerinin daha hızlı ve farklı yöntemlerle şekillenmesine neden olmaktadır. Dolayısıyla ekonomik performansın değişmesinde rol oynayan finansal kurumlardan bankacılık sektöründe meydana gelen dijitalleşme hareketleri, finansal sistemin ve ekonominin geneli için incelenmesi gereken bir konu haline gelmektedir. Dijitalleşmenin kapsamı ve yarattığı etkiler göz önünde bulundurulduğunda, etkinin makroekonomik boyutunun incelenmesi de önem arz etmektedir. Dolayısıyla bu çalışmada, bankacılık sektöründe dijitalleşmenin enflasyon üzerinde yarattığı etkiyi anlamak amacıyla makine öğrenme algoritmaları kullanılarak 2014M01-2023M12 dönemi Türkiye için tahmin odaklı bir model geliştirilmiştir. Yapılan tahminleme sonuçlarına göre, enflasyonu en doğru tahmin eden makine öğrenme algoritmasının gradyan artırma olduğu, mobil bankacılık kullanımının ise enflasyon üzerinde en fazla etkisi olan faktör olarak belirlendiği görülmüştür.

Keywords

Enflasyon tahminleme Dijital bankacılık Makine öğrenme

References

  • Abd El-Aal, M. F. (2023). “Analysis factors affecting Egyptian ınflation based on machine learning algorithms”, Data Science in Finance and Economics, 3(3), 285-304.
  • Ahmad, N., & Schreyer, P. (2016). Are GDP and productivity measures up to the challenges of the digital economy?. International Productivity Monitor, (30), 4.
  • Bakırtaş & Ustaömer, K. (2019). Türkiye’nin bankacılık sektöründe dijitalleşme olgusu, Ekonomi İşletme ve Yönetim Dergisi, 3(1), 1-24.
  • Balsmeier, B., & Woerter, M. (2019). Is this time different? How digitalization influences job creation and destruction. Research policy, 48(8), 103765.
  • Bayrakçı, H. C., Çiçekdemir, R. S., & Özkahraman, M. (2021). Tarım arazilerinde harcanan su miktarını yapay zekâ teknikleri kullanarak belirlenmesi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9(6), 237-250.
  • Beybur, M. (2022). Şubesiz dijital bankacılık ve Türk bankacılık sektörü için öneriler. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 24(1), 286-303.
  • Biau, G., Cadre, B., & Rouvìère, L. (2019). Accelerated gradient boosting. Machine learning, 108, 971-992.
  • Charbonneau, K. B., Evans, A., Sarker, S., & Suchanek, L. (2017). Digitalization and inflation: A review of the literature.
  • Csonto, M. B., Huang, Y., & Mora, M. C. E. T. (2019). Is Digitalization Driving Domestic Inflation?. International Monetary Fund.
  • Emara, N., & Zecheru, D. (2024). Asymmetric threshold effects of digitization on inflation in emerging markets. Financial Innovation, 10(1), 32.
  • Gasparovich, E. O., Uskova, E. V., & Dongauzer, E. V. (2021). The impact of digitalization on employee engagement. In Digital Economy and the New Labor Market: Jobs, Competences and Innovative HR Technologies (pp. 143-150). Springer International Publishing.
  • Gbawae, N. C., & Tonye, T. (2023). The impact of cashless economy on inflation and corruption in Nigeria. Glob Acad J Humanit Soc Sci, 5.
  • Geanakoplos, J., & Dubey, P. (2010). Credit cards and inflation. Games and Economic Behavior, 70(2), 325-353.
  • Göv, A., & Salihoglu, E. (2020). Türkiye’de ekonomik göstergeler ve para arzının bireysel kredi kartı kullanımına etkileri. The Journal of International Scientific Researches, 5(1), 50-63.
  • Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003). KNN model-based approach in classification. In On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, Catania, Sicily, Italy, November 3-7, 2003. Proceedings (pp. 986-996). Springer Berlin Heidelberg.
  • Jadhav, S. D., & Channe, H. P. (2016). Comparative study of K-NN, naive Bayes and decision tree classification techniques. International Journal of Science and Research (IJSR), 5(1), 1842-1845.
  • Kabaklarli, E. (2015). Türkiye’de kredi kartı kullanımının, para politikasındaki rolü ve etkileri. Sosyoekonomi, 23(26), 119-138.
  • Kavzoğlu, T., & Çölkesen, İ. (2010). Karar ağaçları ile uydu görüntülerinin sınıflandırılması. Harita Teknolojileri Elektronik Dergisi, 2(1), 36-45.
  • Katusiime, L. (2018). Private sector credit and inflation volatility. Economies, 6(2), 1-13.
  • Li, Y. S., Pai, P. F., & Lin, Y. L. (2023). “Forecasting Inflation Rates Be Extreme Gradient Boosting with the Genetic Algorithm”, Journal of Ambient Intelligence and Humanized Computing, 14(3), 2211-2220.
  • Lv, L., Liu, Z., & Xu, Y. (2019). Technological progress, globalization and low-inflation: Evidence from the United States. PloS one, 14(4), e0215366.
  • Maharani, D. P.., Romiza, N., Irfan, S., & Febriani, R. E. (2023). Impact of digital payment on economic growth: evidence from Indonesia. Bicemba, 1(1), 233–239.
  • Matolcsy, G., Nagy, M., Palotai, D., & Virág, B. (2020). Inflation in the digital age: Inflation measurement and bias in the 21st Century. Financial and Economic Review, 19(1), 5-36.
  • Mićić, L. (2017). Digital transformation and its influence on GDP. Economics-Innovative and Economics Research Journal, 5(2), 135-147.
  • Mirza, N., Rizvi, S. K. A., Naqvi, B., & Umar, M. (2024). Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting. International Review of Financial Analysis, 94, 103238.
  • Nas, S., Akboz Caner, A. A., & Ünal, A. E. Türkiye’de enflasyon oranlarının makine öğrenme yöntemi ile tahmini. Gaziantep University Journal of Social Sciences, 23(3), 1029-1045.
  • Naumova, O. A., Svetkina, I. A., & Korneeva, T. A. (2020). The impact of digitalization on the economic security index of GDP. In Digital Age: Chances, Challenges and Future 7 (pp. 159-164). Springer International Publishing.
  • Nkemnole, E. B., Wulu, J. T., & Osubu, I. (2024). Application of K-Nearest Neighbours and Long-Short-Term Memory Models using Hidden Markov Model to Predict Inflation Rate and Transition Patterns in Nigeria. Journal of Applied Sciences and Environmental Management, 28(6), 1913-1925.
  • Rahim, F. K., Asdar, M., Sobarsyah, M., & Nursyamsi, I. (2021). The effect of non-cash payments on inflation rate with cash circulation as an intervening variable during the COVID-19 pandemic. International Journal of Innovative Science and Research Technology, 6(7), 765-768.
  • Ramadhan, A. T., & Sudrajad, O. Y. (2022). A comparative study of banking financial performance before and after the bank digitalization in Indonesia. International Journal of Advanced Research in Economics and Finance, 4(3), 129-141.
  • Reddy, K. S., & Kumarasamy, D. (2015). Is There Any Nexus between Electronic Based Payments in Banking and Inflation? Evidence from India. International Journal of Economics and Finance, 7(9), 85-95.
  • Riksbank (2015): Digitisation and inflation. Monetary Policy Report, Riksbank, February.
  • Safdar, S., & Khan, A. (2013). Financial innovation and monetary policy transmission mechanism in Pakistan. International Journal of Development and Sustainability, 2(1), 390-397.
  • Türkiye Cumhuriyet Merkez Bankası (TCMB, 2024) tarafından sağlanan verilere göre.
  • Titalessy, P. B. (2020). Cashless payments and its impact on inflation. Advances in Social Sciences Research Journal, 7(9), 524-532.
  • Ville, B. (2013). Decision trees. Wiley Interdisciplinary Reviews: Computational Statistics, 5(6), 448–455. doi:10.1002/wics.1278
  • Yi, M. H., & Choi, C. (2005). The effect of the internet on inflation: Panel data evidence. Journal of Policy Modeling, 27(7), 885-889.
  • Yücel, R., Akyıldız, Y., & Er, H. (2023). Dijitalleşmenin finans sektörüne getirdiği yenilikler. Özgür Publications.
  • Zhang, S., & Li, J. (2021). KNN classification with one-step computation. IEEE Transactions on Knowledge and Data Engineering, 35(3), 2711-2723.
  • Xing, W., & Bei, Y. (2019). Medical health big data classification based on KNN classification algorithm. Ieee Access, 8, 28808-28819.
  • Xu, X., Li, S., & Liu, W. H. (2025). Forecasting China's inflation rate: Evidence from machine learning methods. International Review of Finance, 25(1), e70000.
  • Watanabe, C., Naveed, K., Tou, Y., & Neittaanmäki, P. (2018). Measuring GDP in the digital economy: Increasing dependence on uncaptured GDP. Technological Forecasting and Social Change, 137, 226-240.

Detecting the relationship between digital banking and inflation with machine learning algorithms

Year 2025, Issue: 70, 193 - 199, 30.04.2025

Ayşe Akboz Caner , Ayşe Ergin Ünal , Serkan Nas

https://doi.org/10.18070/erciyesiibd.1618053

Abstract

Recently, the growing use of digital technology, particularly in finance, has altered the trajectory of various economic indicators. While these digital technologies provide quick and innovative solutions for financial transactions, they accelerate the dynamics of the economy in new and different ways. Consequently, the digitalization initiatives in the banking sector, a crucial financial institution influencing economic performance, emerge as a topic that warrants examination for the broader financial system and economy. Given the scope and impact of digitalization, it is also vital to explore its macroeconomic ramifications. Hence, this study develops a forecasting-oriented model for Turkey for the period from January 2014 to December 2023, utilizing machine learning algorithms to analyze the influence of digitalization on inflation in the banking sector. The forecasting results indicate that the machine learning algorithm with the highest accuracy in predicting inflation is gradient boosting, while the usage of mobile banking is identified as the factor exerting the greatest influence on inflation.

Keywords

Inflation Forecasting Digital Banking Machine Learning

References

  • Abd El-Aal, M. F. (2023). “Analysis factors affecting Egyptian ınflation based on machine learning algorithms”, Data Science in Finance and Economics, 3(3), 285-304.
  • Ahmad, N., & Schreyer, P. (2016). Are GDP and productivity measures up to the challenges of the digital economy?. International Productivity Monitor, (30), 4.
  • Bakırtaş & Ustaömer, K. (2019). Türkiye’nin bankacılık sektöründe dijitalleşme olgusu, Ekonomi İşletme ve Yönetim Dergisi, 3(1), 1-24.
  • Balsmeier, B., & Woerter, M. (2019). Is this time different? How digitalization influences job creation and destruction. Research policy, 48(8), 103765.
  • Bayrakçı, H. C., Çiçekdemir, R. S., & Özkahraman, M. (2021). Tarım arazilerinde harcanan su miktarını yapay zekâ teknikleri kullanarak belirlenmesi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 9(6), 237-250.
  • Beybur, M. (2022). Şubesiz dijital bankacılık ve Türk bankacılık sektörü için öneriler. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 24(1), 286-303.
  • Biau, G., Cadre, B., & Rouvìère, L. (2019). Accelerated gradient boosting. Machine learning, 108, 971-992.
  • Charbonneau, K. B., Evans, A., Sarker, S., & Suchanek, L. (2017). Digitalization and inflation: A review of the literature.
  • Csonto, M. B., Huang, Y., & Mora, M. C. E. T. (2019). Is Digitalization Driving Domestic Inflation?. International Monetary Fund.
  • Emara, N., & Zecheru, D. (2024). Asymmetric threshold effects of digitization on inflation in emerging markets. Financial Innovation, 10(1), 32.
  • Gasparovich, E. O., Uskova, E. V., & Dongauzer, E. V. (2021). The impact of digitalization on employee engagement. In Digital Economy and the New Labor Market: Jobs, Competences and Innovative HR Technologies (pp. 143-150). Springer International Publishing.
  • Gbawae, N. C., & Tonye, T. (2023). The impact of cashless economy on inflation and corruption in Nigeria. Glob Acad J Humanit Soc Sci, 5.
  • Geanakoplos, J., & Dubey, P. (2010). Credit cards and inflation. Games and Economic Behavior, 70(2), 325-353.
  • Göv, A., & Salihoglu, E. (2020). Türkiye’de ekonomik göstergeler ve para arzının bireysel kredi kartı kullanımına etkileri. The Journal of International Scientific Researches, 5(1), 50-63.
  • Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003). KNN model-based approach in classification. In On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, Catania, Sicily, Italy, November 3-7, 2003. Proceedings (pp. 986-996). Springer Berlin Heidelberg.
  • Jadhav, S. D., & Channe, H. P. (2016). Comparative study of K-NN, naive Bayes and decision tree classification techniques. International Journal of Science and Research (IJSR), 5(1), 1842-1845.
  • Kabaklarli, E. (2015). Türkiye’de kredi kartı kullanımının, para politikasındaki rolü ve etkileri. Sosyoekonomi, 23(26), 119-138.
  • Kavzoğlu, T., & Çölkesen, İ. (2010). Karar ağaçları ile uydu görüntülerinin sınıflandırılması. Harita Teknolojileri Elektronik Dergisi, 2(1), 36-45.
  • Katusiime, L. (2018). Private sector credit and inflation volatility. Economies, 6(2), 1-13.
  • Li, Y. S., Pai, P. F., & Lin, Y. L. (2023). “Forecasting Inflation Rates Be Extreme Gradient Boosting with the Genetic Algorithm”, Journal of Ambient Intelligence and Humanized Computing, 14(3), 2211-2220.
  • Lv, L., Liu, Z., & Xu, Y. (2019). Technological progress, globalization and low-inflation: Evidence from the United States. PloS one, 14(4), e0215366.
  • Maharani, D. P.., Romiza, N., Irfan, S., & Febriani, R. E. (2023). Impact of digital payment on economic growth: evidence from Indonesia. Bicemba, 1(1), 233–239.
  • Matolcsy, G., Nagy, M., Palotai, D., & Virág, B. (2020). Inflation in the digital age: Inflation measurement and bias in the 21st Century. Financial and Economic Review, 19(1), 5-36.
  • Mićić, L. (2017). Digital transformation and its influence on GDP. Economics-Innovative and Economics Research Journal, 5(2), 135-147.
  • Mirza, N., Rizvi, S. K. A., Naqvi, B., & Umar, M. (2024). Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting. International Review of Financial Analysis, 94, 103238.
  • Nas, S., Akboz Caner, A. A., & Ünal, A. E. Türkiye’de enflasyon oranlarının makine öğrenme yöntemi ile tahmini. Gaziantep University Journal of Social Sciences, 23(3), 1029-1045.
  • Naumova, O. A., Svetkina, I. A., & Korneeva, T. A. (2020). The impact of digitalization on the economic security index of GDP. In Digital Age: Chances, Challenges and Future 7 (pp. 159-164). Springer International Publishing.
  • Nkemnole, E. B., Wulu, J. T., & Osubu, I. (2024). Application of K-Nearest Neighbours and Long-Short-Term Memory Models using Hidden Markov Model to Predict Inflation Rate and Transition Patterns in Nigeria. Journal of Applied Sciences and Environmental Management, 28(6), 1913-1925.
  • Rahim, F. K., Asdar, M., Sobarsyah, M., & Nursyamsi, I. (2021). The effect of non-cash payments on inflation rate with cash circulation as an intervening variable during the COVID-19 pandemic. International Journal of Innovative Science and Research Technology, 6(7), 765-768.
  • Ramadhan, A. T., & Sudrajad, O. Y. (2022). A comparative study of banking financial performance before and after the bank digitalization in Indonesia. International Journal of Advanced Research in Economics and Finance, 4(3), 129-141.
  • Reddy, K. S., & Kumarasamy, D. (2015). Is There Any Nexus between Electronic Based Payments in Banking and Inflation? Evidence from India. International Journal of Economics and Finance, 7(9), 85-95.
  • Riksbank (2015): Digitisation and inflation. Monetary Policy Report, Riksbank, February.
  • Safdar, S., & Khan, A. (2013). Financial innovation and monetary policy transmission mechanism in Pakistan. International Journal of Development and Sustainability, 2(1), 390-397.
  • Türkiye Cumhuriyet Merkez Bankası (TCMB, 2024) tarafından sağlanan verilere göre.
  • Titalessy, P. B. (2020). Cashless payments and its impact on inflation. Advances in Social Sciences Research Journal, 7(9), 524-532.
  • Ville, B. (2013). Decision trees. Wiley Interdisciplinary Reviews: Computational Statistics, 5(6), 448–455. doi:10.1002/wics.1278
  • Yi, M. H., & Choi, C. (2005). The effect of the internet on inflation: Panel data evidence. Journal of Policy Modeling, 27(7), 885-889.
  • Yücel, R., Akyıldız, Y., & Er, H. (2023). Dijitalleşmenin finans sektörüne getirdiği yenilikler. Özgür Publications.
  • Zhang, S., & Li, J. (2021). KNN classification with one-step computation. IEEE Transactions on Knowledge and Data Engineering, 35(3), 2711-2723.
  • Xing, W., & Bei, Y. (2019). Medical health big data classification based on KNN classification algorithm. Ieee Access, 8, 28808-28819.
  • Xu, X., Li, S., & Liu, W. H. (2025). Forecasting China's inflation rate: Evidence from machine learning methods. International Review of Finance, 25(1), e70000.
  • Watanabe, C., Naveed, K., Tou, Y., & Neittaanmäki, P. (2018). Measuring GDP in the digital economy: Increasing dependence on uncaptured GDP. Technological Forecasting and Social Change, 137, 226-240.
There are 42 citations in total.

Details

Primary Language Turkish
Subjects Financial Economy
Journal Section Makaleler
Authors

Ayşe Akboz Caner 0000-0002-0060-2007

Ayşe Ergin Ünal 0009-0004-5084-6431

Serkan Nas 0000-0002-0040-3091

Early Pub Date April 25, 2025
Publication Date April 30, 2025
Submission Date January 11, 2025
Acceptance Date April 9, 2025
Published in Issue Year 2025 Issue: 70

Cite

APA Akboz Caner, A., Ergin Ünal, A., & Nas, S. (2025). Dijital bankacılık ve enflasyon ilişkisinin makine öğrenme algoritmaları ile tespiti. Erciyes Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi(70), 193-199. https://doi.org/10.18070/erciyesiibd.1618053
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