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Research on the Application of Big Data Technology in Financial Risk Management

Author(s)

Meifang Jiang

Affiliation(s)

Suzhou Agricultural Institute, Suzhou, JiangSu, China

Abstract

This article focuses on the empowering value of big data technology in financial risk management, aiming to break through the limitations of traditional risk control data coverage being narrow, response lagging, and low correlation. Firstly, from a theoretical perspective, this paper analyzes the three major shortcomings of traditional financial risk control that rely on structured data and static models. It elaborates on the adaptability of big data technology in alleviating information asymmetry (such as increasing the credit evaluation coverage of small and micro enterprises from 35% to 82%), achieving comprehensive risk collaborative monitoring (reducing cross risk warning response to 12 minutes), and improving risk pricing accuracy (reducing bad debt rates by 18%) through multi-source data integration, real-time processing, and intelligent analysis. Secondly, based on practical cases, analyze the application effectiveness of big data in four types of risk control: in the field of credit risk, the optimization evaluation and approval of the "Puyin Point Loan" model of Shanghai Pudong Development Bank and the "310" model of online commercial banks; In the field of market risk, platforms such as CITIC Securities and Bank of China have implemented forward-looking warnings; In the field of operational risk, China Merchants Bank's anti fraud system and Industrial and Commercial Bank of China's monitoring platform achieve pre prevention and control; In the field of systemic risk, the financial risk correlation graph helps with global monitoring. Furthermore, it points out three major challenges: data (quality defects, compliance pressure), models (poor interpretability, insufficient robustness), and resources (high investment, talent shortage). Finally, it proposes the trends of technology integration (federated learning, knowledge graph), scenario diversification (ESG risk control, cross-border risk control), and regulatory collaboration (intelligent compliance, regulatory sandbox), as well as suggestions for financial institutions to build in stages and regulatory departments to improve frameworks, providing reference for promoting the transformation of risk control from "experience driven" to "data-driven".

Keywords

Big Data, Financial Risk, Management

References

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