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Article http://dx.doi.org/10.26855/acc.2026.09.012

Research on Enterprise Data Governance Architecture Optimization and Risk Control in the Context of Financial Technology

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Jiaqing Zhang

Small Business Banking, Capital One, McLean, VA 22102, USA.

*Corresponding author: Jiaqing Zhang

Published: August 31,2026

Abstract

Due to the development of fintech, the enterprise data scale and business fields have greatly increased, but it has also caused important structural problems in data governance and risk control. The incomplete architectural structure, different data management standards, and unclear hierarchical responsibilities result in the existence of many data silos and low-efficiency use of assets in most companies. The current risk control methods have some shortcomings, such as a lack of comprehensive risk monitoring throughout the whole process, an inability to identify risks intelligently and respond dynamically to early warnings, which are unable to meet the requirements of new data risks in innovative financial businesses. The main reasons for these problems include backward management concepts, imperfect institutional systems, and inadequate technical abilities in the enterprise’s digital transformation. A multi-level integrated data governance framework involving strategy, management, and business levels can overcome the governance defects of decentralization. By combining optimized governance components and intelligent risk control models with whole-process risk identification and hierarchical early warning mechanisms, the standardization, intelligence, and safety of enterprise data operations are improved effectively, thus enhancing the ability of enterprises to resist fintech data risks.

Keywords

Fintech; data governance architecture; data risk control; intelligent governance

References

[1] Pan M. FinTech innovation, data transparency, and corporate financing quality: an empirical research based on financial and economic data. Probe Account Audit Tax. 2026;8(1).
doi:10.59429/PAAT.V8I1.13643 

[2] Luo H, Xu J, Zhu L, et al. Can data assetisation boost corporate investment efficiency in the FinTech context? Sustainability. 2026;18(8):3763.
doi:10.3390/SU18083763 

[3] Setiawan B, Rani S, Emilda E, et al. Understanding FinTech adoption drivers for digital financial sustainability in urban and rural MSMEs. Risks. 2026;14(4):77.
doi:10.3390/RISKS14040077 

[4] Zhou X, Sun X, Zhang H. Unlocking digital transformation in industrial enterprises: evidence from technology finance. Systems. 2026;14(2):207.
doi:10.3390/SYSTEMS14020207 

[5] Zhu M, Zhang Y, Wu F. FinTech–real economy matching and corporate digital–physical technology integration. Manage Decis Econ. 2026;47(4):1033-1048.
doi:10.1002/MDE.70075 

[6] Na J, Kamaruddina BHB. Digital transformation of Chinese logistics enterprises: impact on corporate innovation from the perspective of Fintech. Account Corp Manag. 2026;8(1).
doi:10.23977/ACCCM.2026.080101 

[7] Wu H, Yang Y, Wang J. Flexible FinTech regulation and corporate digital innovation: evidence from a quasi-natural experiment. Res Int Bus Finance. 2026;82:103262.
doi:10.1016/J.RIBAF.2025.103262 

[8] Rong Y, Hu J. How does FinTech influence enterprises’ digital transformation?—a perspective based on executive backgrounds. Appl Econ. 2025;57(60):10850-10863.
doi:10.1080/00036846.2024.2449213 

How to cite this paper

Research on Enterprise Data Governance Architecture Optimization and Risk Control in the Context of Financial Technology

How to cite this paper: Jiaqing Zhang. (2026) Research on Enterprise Data Governance Architecture Optimization and Risk Control in the Context of Financial Technology. Advances in Computer and Communication, 7(3), 172-175.

DOI: http://dx.doi.org/10.26855/acc.2026.09.012