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Research on the Construction of a Big Data-Driven Precision Prevention and Control System for Financial Risks

Jiaxin Yang

Abstract


The deepening development of digital financial industries has led to risks characterized by concealment, chain transmission, and
cross-domain contagion, posing structural bottlenecks for traditional risk control models in terms of data dimensions, response speed, and
predictive accuracy. By integrating multi-source data and iterating intelligent algorithms, big data technology drives the transformation of
risk prevention and control from "post-event handling" to "pre-event warning." This paper elucidates the theoretical logic and technical architecture of big data risk control, analyzes practical challenges such as data silos, algorithmic black boxes, privacy compliance, and regulatory
coordination, and constructs a closed-loop prevention and control system from four dimensions data governance, intelligent decision-making,
prevention and control tools, and institutional safeguards to provide a practical reference for the modernization of financial risk governance.

Keywords


Big data; Financial risk; Precise prevention and control; Intelligent risk management; Data governance; Collaborative governance

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References


[1] Xuan Di, Ni Koushan. The generation logic and regulatory construction of systemic risk in large fintech companies [J]. Exploration of

Financial Theory, 2026, (01): 68-80.

[2] Wen Ke. The Application of Big Data Analysis Technology in Financial Investment Risk Management [J]. China Science and Technology Investment, 2025, (22): 37-39.

[3] Wang Zezhou. Research on Deep Learning Asset Pricing Driven by Multi source Heterogeneous Mixing Big Data [D]. Hefei University

of Technology, 2025.

[4] Sun Hanyang. Research on Regulatory Governance of Financial Risks in Data Driven Commercial Banks [D]. Southwest University of

Finance and Economics, 2025.




DOI: http://dx.doi.org/10.70711/memf.v3i8.9719

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