Abstract
Türkiye'nin fintek sektörü ilginç bir gelişim göstermektedir. Başlangıçta hızlı büyüme ve yenilik ile karakterize olan sektör, ilerleyen yıllara doğru belirgin bir konsolidasyon sürecine girmiştir. 2016-2024 arası Türkiye fintek yatırım ortamı incelendiğinde 2016 yılında 12 milyon dolar gibi sade bir rakamdan 2024 yılına kadar 194 milyon dolar gibi etkileyici bir rakama ulaşarak kayda değer bir esneklik ve büyüme potansiyeli göstermiştir. Bu da sekiz yıllık dönemde %1.52'lik olağanüstü bir artışı temsil etmektedir. Dolayısıyla bu süreçte Türkiye’nin fintek ekosisteminin büyüme aşamasından olgunlaşma aşamasına geçtiğini, pazarın konsolide olmaya başladığını ve daha az sayıda ancak daha büyük oyuncuların ortaya çıktığı ileri sürülebilir. Bu çalışmada fintek yatırımlarının gelecek analizini gerçekleştirme ve bu amaçla fintek sektörünün büyüme potansiyelinin ortaya koyulması hedeflenmektedir. Bu amaç doğrultusunda Türkiye’de 2016-2024 arası yılları için raporlanan fintek verileri ARIMA modeli kullanılarak hem 2024 öncesi hem de 2025-2028 arası için tahmin yapılmıştır. Elde edilen regresyon tahmini incelendiğinde AR(1) ve MA(1) katsayısı negatif ve istatistiksel olarak anlamlı olmadığı ortaya koyulmuştur. Ancak fintek değişkeninin gelecekteki değerlerini tahmin etmekte bu model sınırlı fayda sağlasa da modelin bir bütün olarak anlamlı olması ve otokorelasyon sorununun olmaması modelin genel performansının uygulanabilir ve geçerli sayılacağı söylenebilir. Regresyon sonrası yapılan tahminde fintek yatırımlarının 2025 yılında 212,1 milyon dolar ile yeni bir zirveye ulaşması ve 2026 yılında 167,1 milyon dolar ile güçlü ivme sürdürmesi beklenmektedir. Tahminler 2027'de potansiyel bir pazar düzeltmesine işaret etse de bu durum yıllarca süren büyümenin ardından doğal bir konsolidasyon aşamasını temsil etmekte ve 2028’de ise 93,1 milyon dolar büyüme beklenmektedir.
Keywords
References
- Azari, A. (2019). Bitcoin Price Prediction: An ARIMA Approach (arXiv:1904.05315). arXiv. https://doi.org/10.48550/arXiv.1904.05315
- Barba Navaretti, G., Calzolari, G., Mansilla-Fernández, J., & Pozzolo, A. F. (Eds.). (2018). Banks, Regulation, and The Real Sector. In Fintech and Banking. Friends or Foes? (p. 162). European Economy.
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- Canbaz, M. F., & Erbaş, S. (2021). Katılım Bankacılığında FinTek Eğilimleri ve Sektöre Katkıları. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 6(3), Article 3. https://doi.org/10.29106/fesa.977015
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- Meher, B. K., Singh, M., Birau, R., & Anand, A. (2024). Forecasting stock prices of fintech companies of India using random forest with high-frequency data. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100180. https://doi.org/10.1016/j.joitmc.2023.100180
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- Ruhana, N. (2023). Forecasting Analysis of the Development of Fintech Lending Financial Performance in Indonesia. Indonesian Journal of Economics and Management, 4(1), Article 1. https://doi.org/10.35313/ijem.v4i1.5497
- Tut, D. (2023). FinTech and the COVID-19 pandemic: Evidence from electronic payment systems. Emerging Markets Review, 54, 100999. https://doi.org/10.1016/j.ememar.2023.100999
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- Zheng, J., Xin, D., Cheng, Q., Tian, M., & Yang, L. (2024). The Random Forest Model for Analyzing and Forecasting the US Stock Market in the Context of Smart Finance. https://doi.org/10.48550/arXiv.2402.17194
- Zhu, J. (2023). Bitcoin Price Prediction: ARIMA & SARIMA vs Linear Regression. Advances in Economics, Management and Political Sciences, 61, 47–54. https://doi.org/10.54254/2754-1169/61/20230776
Abstract
Türkiye's fintech sector has undergone an interesting journey. Initially characterised by rapid growth and innovation, the sector has undergone a significant consolidation process in the following years. When analysing the fintech investment ecosystem in Türkiye between 2016 and 2024, it has shown remarkable flexibility and growth potential, reaching an impressive figure of $194 million by 2024, up from a modest figure of $12 million in 2016. This represents an extraordinary increase of 1.52 percent in an eight-year. Therefore, it can be assumed that the fintech ecosystem in Türkiye has moved from the growth phase to the maturity phase, the market has started to consolidate, and fewer but larger players have emerged. The aim of this paper is to analyse the future of fintech investments and, for this purpose, to identify the growth potential of the fintech sector. For this objective, the fintech data reported for the years between 2016 and 2024 in Türkiye has been estimated both before 2024 and between 2025 and 2028 using the ARIMA model. The analysis of the obtained regression estimation shows that the coefficients of AR(1) and MA(1) are negative and statistically insignificant. However, although this model provides limited benefit in predicting the future values of the fintech variable, it can be said that the overall performance of the model can be considered applicable and valid because the model is significant as a whole and there is no autocorrelation problem. As a result, fintech investment is expected to reach a new peak of $212.1 million in 2025 and maintain a strong momentum of $167.1 million in 2026. Although forecasts point to a potential market correction in 2027, this is a natural consolidation phase after years of growth, and growth of $93.1 million is expected in 2028.
References
- Azari, A. (2019). Bitcoin Price Prediction: An ARIMA Approach (arXiv:1904.05315). arXiv. https://doi.org/10.48550/arXiv.1904.05315
- Barba Navaretti, G., Calzolari, G., Mansilla-Fernández, J., & Pozzolo, A. F. (Eds.). (2018). Banks, Regulation, and The Real Sector. In Fintech and Banking. Friends or Foes? (p. 162). European Economy.
- Bayram, O., Talay, I., & Feridun, M. (2022). Can Fintech Promote Sustainable Finance? Policy Lessons from the Case of Turkey. Sustainability, 14(19), 12414. https://doi.org/10.3390/su141912414
- Benzekri, M. K., & Özütler, H. Ş. (2021). On the Predictability of Bitcoin Price Movements: A Short-term Price Prediction with ARIMA. İktisat Politikası Araştırmaları Dergisi, 8(2), 293-309. DOI: 10.26650/JEPR.946081
- Box, G. E. P., & Pierce, D. A. (1970). Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models. Journal of the American Statistical Association, 65(332), 1509–1526. https://doi.org/10.2307/2284333
- Box, George. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5.th ed.). John Wiley & Sons.
- Bu, Y., Du, X., Li, H., Yu, X., & Wang, Y. (2023). Research on the FinTech risk early warning based on the MS-VAR model: An empirical analysis in China. Global Finance Journal, 58, 100898. https://doi.org/10.1016/j.gfj.2023.100898
- Canbaz, M. F., & Erbaş, S. (2021). Katılım Bankacılığında FinTek Eğilimleri ve Sektöre Katkıları. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 6(3), Article 3. https://doi.org/10.29106/fesa.977015
- Das, S. R. (2019). The future of fintech. Financial Management, 48(4), 981–1007. https://doi.org/10.1111/fima.12297
- Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series With a Unit Root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348
- Erden, B., & Topal, B. (2021). Türkiye’de ve Dünyada İslami Fintek sektörünün gelişimi. Ardahan Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 3(1), 69-75.
- Eylasov, N., & Çiçek, M. (2024). Kripto Para Fiyatlarının Tahmini: ARIMA-GARCH ve LSTM Yöntemlerinin Karşılaştırılması. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 9(1), Article 1. https://doi.org/10.29106/fesa.1380870
- Financial Stability Board. (2017). Financial Innovation. https://www.fsb.org/work-of-the-fsb/financial-innovation-and-structural-change/fintech/
- Gürbüz, A., Kılıç, M., & Tatlıyer, E. (2024). The Impact of Fintech on Economic Growth: Evidence from Panels of Turkic and Southeast Asian Countries. Istanbul Business Research, 53(2), 229–248. https://doi.org/10.26650/ibr.2024.53.1299620
- Karasu, S., Altan, A., Saraç, Z., & Hacioğlu, R. (2018). Prediction of Bitcoin prices with machine learning methods using time series data. 2018 26th Signal Processing and Communications Applications Conference (SIU), 1–4. https://doi.org/10.1109/SIU.2018.8404760
- Kömürcüoğlu, Ö. F., & Akyazı, H. (2020). Finansal Teknolojilerdeki (Fintek) Gelişmeler: Fırsatlar ve Riskler. Karadeniz Ekonomi Araştırmaları Dergisi, 1(1), 35-55. 10.33399/biibfad.1463920
- Li, S., & Xie, N. (2025). The impact of digital finance on firms’ digital Transformation: Mechanism analysis based on enterprise financing. International Review of Economics & Finance, 101, 104223. https://doi.org/10.1016/j.iref.2025.104223
- Mavlutova, I., Spilbergs, A., Verdenhofs, A., Natrins, A., Arefjevs, I., & Volkova, T. (2023). Digital Transformation as a Driver of the Financial Sector Sustainable Development: An Impact on Financial Inclusion and Operational Efficiency. Sustainability, 15(1), 207. https://doi.org/10.3390/su15010207
- Meher, B. K., Singh, M., Birau, R., & Anand, A. (2024). Forecasting stock prices of fintech companies of India using random forest with high-frequency data. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100180. https://doi.org/10.1016/j.joitmc.2023.100180
- Privara, A., Mészáros, R., & Rahmat, N. R. (2025). From digital investment to economic performance: Insights from EU25 economies. Review of Accounting and Finance, 24(4), 568–584. https://doi.org/10.1108/RAF-02-2025-0049
- Ruhana, N. (2023). Forecasting Analysis of the Development of Fintech Lending Financial Performance in Indonesia. Indonesian Journal of Economics and Management, 4(1), Article 1. https://doi.org/10.35313/ijem.v4i1.5497
- Tut, D. (2023). FinTech and the COVID-19 pandemic: Evidence from electronic payment systems. Emerging Markets Review, 54, 100999. https://doi.org/10.1016/j.ememar.2023.100999
- Türkiye Finance Office. (2025). Fintech Snapshot For Türkiye (p.1). https://www.cbfo.gov.tr/sites/default/files/2025-03/fintech-snapshot-v4.3-english_0.pdf
- Umarbeyli, Ş., & Arabacıoğlu, E. (2025). Fintek Hizmetlerinin Türkiye Cumhuriyeti’ndeki Finansal Kuruluşlardaki Rolleri. LAÜ Sosyal Bilimler Dergisi, 16(1), 89–117.
- Zheng, J., Xin, D., Cheng, Q., Tian, M., & Yang, L. (2024). The Random Forest Model for Analyzing and Forecasting the US Stock Market in the Context of Smart Finance. https://doi.org/10.48550/arXiv.2402.17194
- Zhu, J. (2023). Bitcoin Price Prediction: ARIMA & SARIMA vs Linear Regression. Advances in Economics, Management and Political Sciences, 61, 47–54. https://doi.org/10.54254/2754-1169/61/20230776
Details
| Primary Language | English |
|---|---|
| Subjects | Finance |
| Journal Section | Research Article |
| Authors | |
| Submission Date | April 21, 2025 |
| Acceptance Date | November 26, 2025 |
| Publication Date | January 26, 2026 |
| DOI | https://doi.org/10.37880/cumuiibf.1680756 |
| IZ | https://izlik.org/JA66WY92XM |
| Published in Issue | Year 2026 Volume: 27 Issue: 1 |
Cumhuriyet University Journal of Economics and Administrative Sciences is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY NC).