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

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.