Research on Big Data Promoting the Construction of Credit Risk Prevention and Control System for Sichuan Commercial Banks

Maojia Peng; Yan Wang; Yixin Tan; Yanbo Wang; Guoqing Chen

doi:10.47297/wspdecWSP2515-797312.20250912

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Research on Big Data Promoting the Construction of Credit Risk Prevention and Control System for Sichuan Commercial Banks

Volume 9, Issue 12

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Research on Big Data Promoting the Construction of Credit Risk Prevention and Control System for Sichuan Commercial Banks
Research on Big Data Promoting the Construction of Credit Risk Prevention and Control System for Sichuan Commercial Banks
School of Finance, Chengdu Jincheng College, Chengdu, Sichuan, 611731, China
Vol. 9, Issue 12, Pages: 55-59(2025)
Published：30 December 2025
DOI：10.47297/wspdecWSP2515-797312.20250912
Accepted：

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Peng Maojia,Wang Yan,Tan Yixin,et al.Research on Big Data Promoting the Construction of Credit Risk Prevention and Control System for Sichuan Commercial Banks[J].Development Economics of China,2025,09(12):55-59.
DOI：

Peng Maojia,Wang Yan,Tan Yixin,et al.Research on Big Data Promoting the Construction of Credit Risk Prevention and Control System for Sichuan Commercial Banks[J].Development Economics of China,2025,09(12):55-59. DOI： 10.47297/wspdecWSP2515-797312.20250912.

摘要

Abstract

This study examines the application of big data technology in risk management for commercial banks in Sichuan Province. Through literature review and case analysis

it reveals that while progress has been made in risk control infrastructure

challenges persist including an incomplete credit system

delayed information processing

prominent data silos

and low staff competency. Recent regulatory penalties—including a 4.8 million yuan fine imposed on a joint-stock commercial bank's Chengdu branch for systematic violations and a 7.28 million yuan penalty against a provincial-level city commercial bank in early 2026—highlight systemic deficiencies in compliance management and risk control frameworks. By establishing multidimensional data foundations

automating approval processes with intelligent early warnings

implementing tiered response mechanisms across workflows

and enhancing system integration and standardization

big data technology can drive a transition from "experience-driven" to "data intelligence-powered" risk management. Accordingly

recommendations are proposed in five key areas: strengthening data governance

improving risk mechanisms

upgrading staff capabilities

building collaborative ecosystems

and reinforcing regulatory guidance

with the understanding that under unified regulatory standards

different implementation strategies should be adopted based on each bank's specific circumstances. This provides theoretical and practical guidance for constructing an intelligent risk control system.

关键词

Keywords

references

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