FinTech, artificial intelligence, and European Union banks: A double-edged sword for performance?

Abstract

Research background: The ongoing digital transformation within the European Union's banking sector, driven by the adoption of financial technologies (FinTech) and artificial intelli-gence (AI), has introduced substantial opportunities for enhancing operational efficiency, customer service personalization, and financial outreach. However, these innovations also generate strategic complexity, intensify competition from non-bank digital entrants, and create disparities in adaptation capacity across financial institutions. Despite a growing body of literature on this topic, empirical investigations remain limited regarding how these technologies differentially affect banks, based on their existing performance levels.

Purpose of the article: This study examines the extent to which FinTech and AI integration influences financial performance across banks in all 27 EU member states. Specifically, it investigates whether these technological drivers yield uniform effects or whether their impact varies across banks depending on their baseline performance. The analysis is grounded in the premise that FinTech and AI are not inherently per-formance-enhancing and that their effects may depend on context, capacity, and strategy.

Methods: Employing a panel quantile regression model, the analysis is based on a balanced panel dataset spanning the 2017–2023 period. To address both short- and long-term dynamics, the study complements its core estimation with a vector error correction model (VECM) and validates the robustness of findings through sys-tem-based regression techniques. The econometric framework incorporates bank-specific instruments and lagged performance metrics, with a particular focus on return on equity (ROE) as the dependent variable.

Findings & value added: The results indicate that the adoption of FinTech and AI is associated with signif-icant improvements in performance metrics, particularly among well-capitalized or technologically agile banks. However, institutions with weaker fundamentals may experience limited or even adverse effects. These findings suggest that the performance implications of innovation are conditional, not universal. The paper contributes by offering a distribution-sensitive analysis that refines our under-standing of technological transformation in EU banking and provides actionable insights into strategic decision-making and regulatory oversight. Also, the results offer practical implications for bank executives considering strategic technology investments, as well as for regulators aiming to design supportive, risk-sensitive digital finance policies.

Keywords

FinTech, artificial intelligence, bank performance, Panel Quantile Regression, technological innovation

References

1. Aldboush, H. H. H., & Ferdous, M. (2023). Building trust in Fintech: An analysis of ethical and privacy considerations in the intersection of big data, AI, and customer trust. International Journal of Financial Studies, 11(3), 90. DOI: https://doi.org/10.3390/ijfs11030090
   View in Google Scholar
2. Alkadi, R. S., & Abed, S. S. (2025). AI in banking: What drives generation Z to adopt AI-enabled voice assistants in Saudi Arabia? International Journal of Financial Studies, 13(1), 36. DOI: https://doi.org/10.3390/ijfs13010036
   View in Google Scholar
3. Ally, A. M. (2025). Artificial intelligence (AI) and financial technology (FinTech) in Tanzania: Legal and regulatory issues. International Journal of Law and Management. DOI: https://doi.org/10.1108/IJLMA-07-2024-0251
   View in Google Scholar
4. Almansour, M. (2023). Artificial intelligence and resource optimization: A study of FinTech start-ups. Resources Policy, 80, 103250. DOI: https://doi.org/10.1016/j.resourpol.2022.103250
   View in Google Scholar
5. Almaqtari, F. A., Yahya, A. T., Al-Maskari, N., Farhan, N. H. S., & Al-Aamri, A. Y. Y. (2025). Assessing the integrated role of IT governance, FinTech, and blockchain in enhancing sustainability performance and mitigating organizational risk. Risks, 13(6), 105. DOI: https://doi.org/10.3390/risks13060105
   View in Google Scholar
6. Andres-Sanchez, J., & Gene-Albesa, J. (2023). Explaining policyholders' chatbot acceptance with a unified technology acceptance and use of technology-based model. Journal of Theoretical and Applied Electronic Commerce Research, 18(3), 1217–1237. DOI: https://doi.org/10.3390/jtaer18030062
   View in Google Scholar
7. Andronie, M., Iatagan, M., Uta, C., Hurloiu, I., Dijmarescu, A., & Dijmarescu, I. (2023). Big data management algorithms in artificial Internet of Things-based FinTech. Oeconomia Copernicana, 14(3), 769–793. DOI: https://doi.org/10.24136/oc.2023.023
   View in Google Scholar
8. Ashta, A., & Herrmann, H. (2021). Artificial intelligence and FinTech: An overview of opportunities and risks for banking, investments, and microfinance. Strategic Change, 30(3), 211–222. DOI: https://doi.org/10.1002/jsc.2404
   View in Google Scholar
9. Baltgailis, J., Simakhova, A., & Buka, S. (2024). AI in banking: Socio-economic aspects. Baltic Journal of Economic Studies, 10(3), 26–35. DOI: https://doi.org/10.30525/2256-0742/2024-10-3-26-35
   View in Google Scholar
10. Barile, D., Secundo, G., & Bussoli, C. (2024). Exploring artificial intelligence robo-advisors in the banking industry: A platform model. Management Decision. DOI: https://doi.org/10.1108/MD-08-2023-1324
    View in Google Scholar
11. Barrodale, I., & Roberts, F. D. K. (1974). Solution of an overdetermined system of equations in the norm. Communications of the ACM, 17(6), 319–320. DOI: https://doi.org/10.1145/355616.361024
    View in Google Scholar
12. Bayram, O., Talay, İ., & Feridun, M. (2022). Can FinTech promote sustainable finance? Policy lessons from the case of Turkey. Sustainability, 14(19), 12414. DOI: https://doi.org/10.3390/su141912414
    View in Google Scholar
13. Bholat, D., & Susskind, D. (2021). The assessment: Artificial intelligence and financial services. Oxford Review of Economic Policy, 37(3), 417–434. DOI: https://doi.org/10.1093/oxrep/grab015
    View in Google Scholar
14. Breitung, J., & Pesaran, M. H. (2008). Unit roots and cointegration in panels. In L. Mátyás & P. Sevestre (Eds.). The econometrics of panel data (Vol. 46, pp. 279–322). Springer. DOI: https://doi.org/10.1007/978-3-540-75892-1_9
    View in Google Scholar
15. Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroskedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294. DOI: https://doi.org/10.2307/1911963
    View in Google Scholar
16. Byambaa, O., Yondon, C., Rentsen, E., Darkhijav, B., & Rahman, M. (2025). An empirical examination of the adoption of artificial intelligence in banking services: The case of Mongolia. Future Business Journal, 11(1), 76. DOI: https://doi.org/10.1186/s43093-025-00504-y
    View in Google Scholar
17. Carè, R., Boitan, I. A., Stoian, A. M., & Fatima, R. (2025). Exploring the landscape of financial inclusion through the lens of financial technologies: A review. Finance Research Letters, 72, 106500. DOI: https://doi.org/10.1016/j.frl.2024.106500
    View in Google Scholar
18. Chatterjee, P., Das, D., & Rawat, D. B. (2024). A generative AI approach for ensuring data integrity security resilience in FinTech systems. In 2024 IEEE 24th international symposium on cluster, cloud and Internet computing workshops. IEEE. DOI: https://doi.org/10.1109/CCGridW63211.2024.00027
    View in Google Scholar
19. Cheng, M., & Qu, Y. (2023). Does operational risk management benefit from FinTech? Emerging Markets Finance and Trade, 59(14), 4012–4027. DOI: https://doi.org/10.1080/1540496X.2022.2164464
    View in Google Scholar
20. Cho, S., Lee, Z., Hwang, S., & Kim, J. (2023). Determinants of bank closures: What ensures sustainable profitability in mobile banking? Electronics, 12(5), 1196. DOI: https://doi.org/10.3390/electronics12051196
    View in Google Scholar
21. de Boyrie, M. E., & Pavlova, I. (2025). Bank acquisitions of AI and FinTech: Impact on performance. Managerial Finance. DOI: https://doi.org/10.2139/ssrn.4706610
    View in Google Scholar
22. Del Sarto, N., Ramirez, I. C., & Gai, L. (2025). Impacts of FinTech funding announcements on traditional banks: An event study analysis. Journal of Economics and Business, 133(C), 106231. DOI: https://doi.org/10.1016/j.jeconbus.2024.106231
    View in Google Scholar
23. Doran, N. M., Manta, A. G., Badareu, G., Berceanu, D., Doran, M. D., & Manta, F. L. (2025). Could e-commerce activities drive to climate change mitigation? Novel evidence from panel quantile regression model. Journal of Business Economics and Management, 26(2), 255–276. DOI: https://doi.org/10.3846/jbem.2025.23604
    View in Google Scholar
24. Doumpos, M., Zopounidis, C., Gounopoulos, D., Platanakis, E., & Zhang, W. (2023). Operational research and artificial intelligence methods in banking. European Journal of Operational Research, 306(1), 1–16. DOI: https://doi.org/10.1016/j.ejor.2022.04.027
    View in Google Scholar
25. Durodola, L. O. (2021). Towards a responsible use of artificial intelligence (AI) and FinTech in modern banking. In A. Lui & N. Ryder (Eds.). FinTech, artificial intelligence and the law: Regulation and crime prevention (pp. 262–276). Abingdon: Routledge. DOI: https://doi.org/10.4324/9781003020998-18
    View in Google Scholar
26. Elgendy, I. A., Helal, M. Y. I., Al-Sharafi, M. A., Albashrawi, M. A., Al-Ahmadi, M. S., Jeon, I., & Dwivedi, Y. K. (2025). Agentic systems as catalysts for innovation in FinTech: Exploring opportunities, challenges and a research agenda. Information Discovery and Delivery. DOI: https://doi.org/10.1108/IDD-03-2025-0068
    View in Google Scholar
27. European Banking Federation. (2024). Impact of AI on banking employment in Europe (Final report). European Banking Federation. Retrieved from https://www.ebf.eu/wp-content/uploads/2024/05/Final-report_Impact-of-AI-on-banking-employment-in-Europe.pdf (20.05.2025).
    View in Google Scholar
28. European Central Bank. (2024). Rapid growth and strategic location: Analysing the rise of FinTechs in the EU. In Financial integration and structure in the euro area 2024. European Central Bank. Retrieved from https://www.ecb.europa.eu/press/fie/box/html/ecb.fiebox202406_08.en.html 14.05.2025.
    View in Google Scholar
29. European Central Bank. (2024). The rise of artificial intelligence: Benefits and risks for financial stability. In Financial stability review (pp. 134–142). European Central Bank. Retrieved from https://www.ecb.europa.eu/press/financial-stability-publications/fsr/special/html/ecb.fsrart202405_02~58c3ce5246.en.html (10.05.2025).
    View in Google Scholar
30. European Commission. (2024). Digital Economy and Society Index (DESI) 2024: Country reports and thematic insights. Brussels: European Commission. Retrieved from https://digital-strategy.ec.europa.eu/en/policies/desi (13.05.2025).
    View in Google Scholar
31. Eurostat. (2024). Digital society statistics at regional level. Retrieved from https://ec.europa.eu/eurostat/statistics-explained/ (13.05.2025)..
    View in Google Scholar
32. Geng, H., Guo, P., & Cheng, M. (2023). The dark side of bank FinTech: Evidence from a transition economy. Economic Analysis and Policy, 80, 1811–1830. DOI: https://doi.org/10.1016/j.eap.2023.11.020
    View in Google Scholar
33. Ghandour, A. (2021). Opportunities and challenges of artificial intelligence in banking: Systematic literature review. TEM Journal, 10(4), 1581–1587. DOI: https://doi.org/10.18421/TEM104-12
    View in Google Scholar
34. Gherțescu, C., Manta, A. G., Bădîrcea, R. M., & Manta, L. F. (2024). How does the digitalization strategy affect bank efficiency in Industry 4.0? A bibliometric analysis. Systems, 12(11), 492. DOI: https://doi.org/10.3390/systems12110492
    View in Google Scholar
35. Granger, C. W. J., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111–120. DOI: https://doi.org/10.1016/0304-4076(74)90034-7
    View in Google Scholar
36. Gross, H. P., Ingerfurth, S., & Willems, J. (2021). Employees as reputation advocates: Dimensions of employee job satisfaction explaining employees’ recommendation intention. Journal of Business Research, 134, 405–413. DOI: https://doi.org/10.1016/j.jbusres.2021.05.021
    View in Google Scholar
37. Gründler, K., & Krieger, T. (2021). Using machine learning for measuring democracy: A practitioner’s guide and a new updated dataset for 186 countries from 1919 to 2019. European Journal of Political Economy, 70, 102047. DOI: https://doi.org/10.1016/j.ejpoleco.2021.102047
    View in Google Scholar
38. Gyau, E. B., Appiah, M., Gyamfi, B. A., Acheampong, T., & Naeem, M. A. B. (2024). Transforming banking: Examining the role of AI technology innovation and banks’ financial performance. International Review of Financial Analysis, 103700. DOI: https://doi.org/10.1016/j.irfa.2024.103700
    View in Google Scholar
39. Haabazoka, L. (2019). A study of the effects of technological innovations on the performance of commercial banks in developing countries: A case of the Zambian banking industry. In E. Popkova (Ed.). The future of the global financial system: Downfall or harmony. ISC 2018. Lecture notes in networks and systems (Vol. 57, pp. 1076–1085). Springer. DOI: https://doi.org/10.1007/978-3-030-00102-5_132
    View in Google Scholar
40. Haddad, C., & Hornuf, L. (2021). The impact of FinTech startups on financial institutions’ performance and default risk. CESifo Working Paper, 9050. DOI: https://doi.org/10.2139/ssrn.3837778
    View in Google Scholar
41. Hassan, M. I. U., Wu, M., Lu, J., Sohu, J. M., Ali, S., Anjum, H. N., & Bilal, M. (2025). Financial technology and banking performance in developing countries: Evidence from an advanced quantile regression approach. Humanities and Social Sciences Communications, 12, 1455. DOI: https://doi.org/10.1057/s41599-025-05571-8
    View in Google Scholar
42. He, D., Leckow, R., Haksar, V., Mancini-Griffoli, T., Jenkinson, N., Kashima, M., Khiaonarong, T., Rochon, C., & Tourpe, H.. (2017). FinTech and financial services: Initial considerations. IMF Staff Discussion Note, 17/05. DOI: https://doi.org/10.5089/9781484322383.006
    View in Google Scholar
43. Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74. DOI: https://doi.org/10.1016/S0304-4076(03)00092-7
    View in Google Scholar
44. Jagtiani, J., & John, K. (2018). FinTech: The impact on consumers and regulatory responses. Journal of Economics and Business, 100, 1–6. DOI: https://doi.org/10.1016/j.jeconbus.2018.11.002
    View in Google Scholar
45. Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255–259. DOI: https://doi.org/10.1016/0165-1765(80)90024-5
    View in Google Scholar
46. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551–1580. DOI: https://doi.org/10.2307/2938278
    View in Google Scholar
47. Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90(1), 1–44. DOI: https://doi.org/10.1016/S0304-4076(98)00023-2
    View in Google Scholar
48. Karthikeyan, G. K., & Bhowmik, B. (2025). Enhancing money laundering detection in bank transactions using GAGAN: A graph-adapted generative adversarial network approach. International Journal of Data Science and Analytics.
    View in Google Scholar
49. Kliestik, T., Dragomir, R., Baluta, A. V., Grecu, I., Durana, P., Karabolevski, O. L., Kral, P., Balica. R., Suler, P., Bușu, O. V., Bugaj, M., Voinea, D.-V., Vrbka, J., Cocoșatu, M., Grupac, M., Pera, A., & Grupac, M. (2024). Enterprise generative AI, blockchain-based FinTech, and the industrial metaverse in the cognitive algorithmic economy. Oeconomia Copernicana, 15(4), 1183–1221. DOI: https://doi.org/10.24136/oc.3109
    View in Google Scholar
50. Koenker, R. (2005). Quantile regression. Cambridge: Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511754098
    View in Google Scholar
51. Koenker, R. W., & D’Orey, V. (1987). Algorithm AS 229: Computing regression quantiles. Applied Statistics, 36(3), 383–393. DOI: https://doi.org/10.2307/2347802
    View in Google Scholar
52. Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50. DOI: https://doi.org/10.2307/1913643
    View in Google Scholar
53. Koenker, R., & Bassett, G., Jr. (1982). Robust tests for heteroskedasticity based on regression quantiles. Econometrica, 50(1), 43–62. DOI: https://doi.org/10.2307/1912528
    View in Google Scholar
54. Koenker, R., & Machado, J. A. F. (1999). Goodness of fit and related inference processes for quantile regression. Journal of the American Statistical Association, 94(448), 1296–1310. DOI: https://doi.org/10.1080/01621459.1999.10473882
    View in Google Scholar
55. Kumar, A. (2024). Redefining finance: The influence of artificial intelligence (AI) and machine learning (ML). Transactions on Engineering and Computing Sciences, 12(4), 59–69.
    View in Google Scholar
56. Kumar, A., Srivastava, A., & Gupta, P. K. (2022). Banking 4.0: The era of artificial intelligence-based FinTech. Strategic Change, 31(6), 591–601. DOI: https://doi.org/10.1002/jsc.2526
    View in Google Scholar
57. Laidroo, L., Koroleva, E., Kliber, A., Rupeika-Apoga, R., & Grigaliuniene, Z. (2021). Business models of FinTechs: Difference in similarity? Electronic Commerce Research and Applications, 46, 101034. DOI: https://doi.org/10.1016/j.elerap.2021.101034
    View in Google Scholar
58. Lazaroiu, G., Bogdan, M., Geamanu, M., Hurloiu, L., Ionescu, L., & Stefanescu, R. (2023). Artificial intelligence algorithms and cloud computing technologies in blockchain-based FinTech management. Oeconomia Copernicana, 14(3), 707–730. DOI: https://doi.org/10.24136/oc.2023.021
    View in Google Scholar
59. Levin, A., Lin, C. F., & Chu, C. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1–24. DOI: https://doi.org/10.1016/S0304-4076(01)00098-7
    View in Google Scholar
60. Li, S. Y., Younas, M. W., Maqsood, U. S., & Zahid, R. M. A. (2024). Impact of AI adoption on ESG performance: Evidence from Chinese firms. Energy & Environment. DOI: https://doi.org/10.1177/0958305X241269041
    View in Google Scholar
61. Machado, J. A. F., & Silva, J. S. (2019). Quantiles via moments. Journal of Econometrics, 213(1), 145–173. DOI: https://doi.org/10.1016/j.jeconom.2019.04.009
    View in Google Scholar
62. Manta, A. G., Badareu, G., Bădîrcea, R. M., & Doran, N. M. (2023). Does banking accessibility matter in assuring the economic growth in the digitization context? Evidence from Central and Eastern European countries. Electronics, 12(2), 279. DOI: https://doi.org/10.3390/electronics12020279
    View in Google Scholar
63. Manta, A. G., Bădîrcea, R. M., Doran, N. M., Badareu, G., Gherțescu, C., & Popescu, J. (2024). Industry 4.0 transformation: Analysing the impact of artificial intelligence on the banking sector through bibliometric trends. Electronics, 13(9), 1693. DOI: https://doi.org/10.3390/electronics13091693
    View in Google Scholar
64. Manta, L. F., Manta, A. G., & Gherțescu, C. (2025). Decoding digital synergies: How mechatronic systems and artificial intelligence shape banking performance through quantile-driven method of moments. Applied Sciences, 15(10), 5282. DOI: https://doi.org/10.3390/app15105282
    View in Google Scholar
65. Maracine, V., Voican, O., & Scarlat, E. (2020). The digital transformation and disruption in business models of the banks under the impact of FinTech and BigTech. Proceedings of the International Conference on Business Excellence, 14(1), 294–305. DOI: https://doi.org/10.2478/picbe-2020-0028
    View in Google Scholar
66. Mathen, M. P., & Paul, A. (2025). Toward an evolving framework for responsible AI for credit scoring in the banking industry. Journal of Information, Communication & Ethics in Society. DOI: https://doi.org/10.1108/JICES-08-2024-0122
    View in Google Scholar
67. McCanless, M. (2023). Banking on alternative credit scores: Auditing the calculative infrastructure of U.S. consumer lending. Environment and Planning A: Economy and Space, 55(8), 1575–1596. DOI: https://doi.org/10.1177/0308518X231174026
    View in Google Scholar
68. Newey, W. K., & Powell, J. L. (1987). Asymmetric least squares estimation. Econometrica, 55(4), 819–847. DOI: https://doi.org/10.2307/1911031
    View in Google Scholar
69. Nicoletti, B. (2018). The future of FinTech: Integrating finance and technology in financial services. Springer. DOI: https://doi.org/10.1007/978-3-319-51415-4_2
    View in Google Scholar
70. Pasiouras, F., & Kosmidou, K. (2007). Factors influencing the profitability of domestic and foreign commercial banks in the European Union. Research in International Business and Finance, 21(2), 222–237. DOI: https://doi.org/10.1016/j.ribaf.2006.03.007
    View in Google Scholar
71. Pattnaik, D., Ray, S., & Raman, R. (2024). Applications of artificial intelligence and machine learning in the financial services industry: A bibliometric review. Heliyon, 10(1), e23492. DOI: https://doi.org/10.1016/j.heliyon.2023.e23492
    View in Google Scholar
72. Pesaran, M. H. (2015). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6–10), 1089–1117. DOI: https://doi.org/10.1080/07474938.2014.956623
    View in Google Scholar
73. Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50–93. DOI: https://doi.org/10.1016/j.jeconom.2007.05.010
    View in Google Scholar
74. Phan, D. H. B., Narayan, P. K., Rahman, R. E., & Hutabarat, A. R. (2020). Do financial technology firms influence bank performance? Pacific-Basin Finance Journal, 62, 101210. DOI: https://doi.org/10.1016/j.pacfin.2019.101210
    View in Google Scholar
75. Phan, T. T. T., Nguyen, V. V., & Lee, C.-H. (2023). Establishing an importance–performance evaluating framework under integrating adaptive capacity for community-based plastic waste management. Frontiers in Environmental Science, 11, 1243084. DOI: https://doi.org/10.3389/fenvs.2023.1243084
    View in Google Scholar
76. Piotrowski, D., & Orzeszko, W. (2023). Artificial intelligence and customers’ intention to use robo-advisory in banking services. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(4), 967–1007. DOI: https://doi.org/10.24136/eq.2023.031
    View in Google Scholar
77. Prakash, N., Singh, S., & Sharma, S. (2025). Technological catch-up, nonmonotonicity, and convergence: Parametric evidence from the BRICS and European banking systems. Economic Systems, 49(1), 101253. DOI: https://doi.org/10.1016/j.ecosys.2024.101253
    View in Google Scholar
78. Riikkinen, M., & Pihlajamaa, M. (2022). Achieving a strategic fit in FinTech collaboration: A case study of Nordea Bank. Journal of Business Research, 152, 461–472. DOI: https://doi.org/10.1016/j.jbusres.2022.05.049
    View in Google Scholar
79. Schrank, J. (2025). The impact of artificial intelligence on behavioral intentions to use mobile banking in the post-COVID-19 era. Frontiers in Artificial Intelligence, 8, 1649392. DOI: https://doi.org/10.3389/frai.2025.1649392
    View in Google Scholar
80. Schulte, P., & Liu, G. (2018). FinTech is merging with IoT and AI to challenge banks: How entrenched interests can prepare. Journal of Alternative Investments, 20(3), 41–57. DOI: https://doi.org/10.3905/jai.2018.20.3.041
    View in Google Scholar
81. Sharbek, N. (2022). How traditional financial institutions have adapted to artificial intelligence, machine learning and FinTech? Proceedings of the International Conference on Business Excellence, 16(1), 837–848. DOI: https://doi.org/10.2478/picbe-2022-0078
    View in Google Scholar
82. Siddik, A. B., Yong, L., Du, A. M., Vigne, S. A., & Sharif, A. (2025). Harnessing artificial intelligence for enhanced environmental sustainability in China's banking sector: A mixed-methods approach. British Journal of Management, 36(3), 1256–1273. DOI: https://doi.org/10.1111/1467-8551.12901
    View in Google Scholar
83. Singh, P., Roy, N., Bhatt, A. S., Sadual, M. K., & Sahai, A. (2024). Consumer’s perspective towards adoption of artificial intelligence: An empirical study among banking companies. Pacific Business Review International, 17(3), 131–138.
    View in Google Scholar
84. Stojaković-Čelustka, S. (2022). FinTech and its implementation. In R. Polovina, S. Polovina, & N. Kemp (Eds.). Measuring ontologies for value enhancement: Aligning computing productivity with human creativity for societal adaptation (MOVE 2020) (pp. 256–277). Springer..
    View in Google Scholar
85. Truby, J., Brown, R., & Dahdal, A. (2020). Banking on AI: Mandating a proactive approach to AI regulation in the financial sector. Law and Financial Markets Review, 14(2), 110–120. DOI: https://doi.org/10.1080/17521440.2020.1760454
    View in Google Scholar
86. Upadhyay, N., & Kamble, A. (2024). Why can’t we help but love mobile banking chatbots? Perspective of stimulus-organism-response. Journal of Financial Services Marketing, 29(3), 855–872. DOI: https://doi.org/10.1057/s41264-023-00237-5
    View in Google Scholar
87. Varma, P., Nijjer, S., Sood, K., Grima, S., & Rupeika-Apoga, R. (2022). Thematic analysis of financial technology (FinTech) influence on the banking industry. Risks, 10(10), 186. DOI: https://doi.org/10.3390/risks10100186
    View in Google Scholar
88. Vives, X. (2019). Digital disruption in banking. Annual Review of Financial Economics, 11(1), 243–272. DOI: https://doi.org/10.1146/annurev-financial-100719-120854
    View in Google Scholar
89. Vučinić, M., & Luburić, R. (2024). Artificial intelligence, FinTech and challenges to central banks. Journal of Central Banking Theory and Practice, 13(3), 5–42. DOI: https://doi.org/10.2478/jcbtp-2024-0021
    View in Google Scholar
90. Wang, X., Deng, Y., & Mao, X. (2025). The impact of bank digital transformation on enterprises’ digital technology innovation in China. International Review of Financial Analysis, 102, 104068. DOI: https://doi.org/10.1016/j.irfa.2025.104068
    View in Google Scholar
91. World Bank. (2024). World Development Indicators 2024. Washington, DC: The World Bank. Retrieved from https://databank.worldbank.org/source/world-development-indicators (13.05.2025).
    View in Google Scholar
92. Xie, J., Chen, L., Liu, Y., & Wang, S. (2023). Does FinTech inhibit corporate greenwashing behavior? Evidence from China. Finance Research Letters, 55(Part B), 104002. DOI: https://doi.org/10.1016/j.frl.2023.104002
    View in Google Scholar
93. Zhang, H., Fahlevi, M., Aljuaid, M., Beşer, N. Ö., Cabas, M., & Lominchar, J. (2024). A machine learning and quantile analysis of FinTech and resource efficiency in achieving sustainable development in OECD countries. Resources Policy, 92, 105017. DOI: https://doi.org/10.1016/j.resourpol.2024.105017
    View in Google Scholar
94. Zhao, J., Li, X., Yu, C.-H., Chen, S., & Lee, C.-C. (2022). Riding the FinTech innovation wave: FinTech, patents and bank performance. Journal of International Money and Finance, 122, 102552. DOI: https://doi.org/10.1016/j.jimonfin.2021.102552
    View in Google Scholar