Convergence of Artificial and Business Intelligence in FinTech: A Quantitative Analysis of Stakeholder Insights on Trust, Risk, and Financial Innovation
DOI:
https://doi.org/10.31305/rrijm.2026.v11.n03.024Keywords:
FinTech Perception, Transparency, Regulation, Trust, Financial Innovation, AIAbstract
With an emphasis on how important elements, perceived risk, system openness, and trust in AI-BI systems affect stakeholder perceptions of financial innovation, this study clarifies the confluence of AI and business intelligence in the FinTech industry. Data was gathered from 248 middle and senior-level individuals working in the FinTech and AI domains using a quantitative research design. The findings showed that stakeholder perception was significantly improved by all three independent variables, with trust acting as the most powerful predictor. The study supports the growing significance of explainability, risk communication, and principled AI practices in endorsing the adoption of innovations. These results advance the theoretical knowledge of technology adoption in data-driven financial services and render useful information for developers, FinTech companies, and legislators looking to boost confidence and lessen opposition to AI-BI-powered advances.
References
[1] Ahuja, V., Akhtar, A., & Wali, O. P. (2024). Development of a comprehensive model of social entrepreneurial intention formation using a quality tool. Journal of Global Entrepreneurship Research, 9(1), 1–27. https://doi.org/10.1186/s40497-019-0164-4 DOI: https://doi.org/10.1186/s40497-019-0164-4
[2] Brogi, M., & Lagasio, V. (2024). New but naughty. The evolution of misconduct in FinTech. International Review of Financial Analysis, 95, 103489. https://doi.org/10.1016/j.irfa.2024.103489 DOI: https://doi.org/10.1016/j.irfa.2024.103489
[3] Cubric, M., & Li, F. (2024). Bridging the ‘Concept–Product’ gap in new product development: Emerging insights from the application of artificial intelligence in FinTech SMEs. Technovation, 134, 103017. https://doi.org/10.1016/j.technovation.2024.103017 DOI: https://doi.org/10.1016/j.technovation.2024.103017
[4] Dadhich, M., Shukla, A., Pahwa, M. S., & Mathur, A. (2024). Decentralized Disruptive Crypto Landscape: How Digital Currencies Are Shaking up Finance? In S. Rajagopal, K. Popat, D. Meva, & S. Bajeja (Eds.), Advancements in Smart Computing and Information Security (pp. 268–282). Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-59107-5_18
[5] Dorfleitner, G., & Braun, D. (2019). Fintech, Digitalization and Blockchain: Possible Applications for Green Finance. https://doi.org/10.1007/978-3-030-22510-0_9 DOI: https://doi.org/10.1007/978-3-030-22510-0_9
[6] Gaurav Kumar Singh & Manish dadhich. (2023). Empirical investigation of industry 4.0 for sustainable growth and implication for future-ready compatibility for cement industry of India. AIP Conference Proceedings 2521, 040026 (2023), 1–12. https://doi.org/978-0-7354-4650-2/$30.00
[7] Hair Jr, Joseph F., G. Tomas M. Hult, Christian Ringle, and M. S. (2016). A primer on partial least squares structural equation modeling (PLS-SEM).
[8] Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. DOI: https://doi.org/10.1007/s11747-014-0403-8
[9] Ho, D. B., Duong, C. D., Tran, M. L., Luong, T. S., & Tran, T. P. H. (2025). Big data analytics powered by artificial intelligence and entrepreneurial resilience: A moderated mediation model of technological turbulence and business innovation model. Journal of Open Innovation: Technology, Market, and Complexity, 11(3), 100611. https://doi.org/10.1016/j.joitmc.2025.100611 DOI: https://doi.org/10.1016/j.joitmc.2025.100611
[10] Manish Dadhich, Bright Akwasi Gyamfi, Festus Victor Bekun, Sadananda Prusty, Simplice A. Asongu. (2025). Why management papers face rejection? An advanced guide to publish manuscripts in high‑impact management journals. Quality & Quantity, 1, 1–15. https://doi.org/10.1007/s11135-025-02221-8 DOI: https://doi.org/10.1007/s11135-025-02221-8
[11] PK Panigrahi, S Sethy, M. D. (2022). An Empirical Evaluation of Internet of Things (IoT) Implementations in the Libraries. RESEARCH REVIEW International Journal of Multidisciplinary, 7(10), 116–124. https://doi.org/10.31305/rrijm.2022.v07.i10.014 DOI: https://doi.org/10.31305/rrijm.2022.v07.i10.014
[12] Secinaro, S., Lanzalonga, F., Oppioli, M., & De Nuccio, E. (2025). The effects of disruptive technologies on accountability in fintech industry: Using bibliometric analysis to develop a research agenda. Research in International Business and Finance, 76, 102816. https://doi.org/10.1016/j.ribaf.2025.102816 DOI: https://doi.org/10.1016/j.ribaf.2025.102816
[13] Zarifis, A., & Cheng, X. (2022). A model of trust in Fintech and trust in Insurtech: How Artificial Intelligence and the context influence it. Journal of Behavioral and Experimental Finance, 36, 100739. https://doi.org/10.1016/j.jbef.2022.100739 DOI: https://doi.org/10.1016/j.jbef.2022.100739