A Research on the Pathways for Preventing Financial Risks in the Social Credit System Based on the Digital Economy

Abstract

Against the backdrop of the deep penetration of the digital economy into the financial field, the social credit reporting system, as the core infrastructure for risk prevention, is crucial to maintaining financial stability. This paper constructs a big social credit data development index (SCDBI) with 5 dimensions and 23 indicators and uses the entropy value method and panel data fixed effect model to empirically analyze the quantitative relationship between the digital level of the credit reporting system and financial risks. The study found that for every unit increase in SCDBI, the regional non-performing loan ratio dropped significantly by 0.72 percentage points (p<0.01), and data infrastructure and digital technology momentum are the core driving factors. Combined with the practical verification of Suzhou Digital Credit Investigation Experimental Zone, the data coverage breadth increased by 22 percentage points to increase the financing satisfaction rate to 92.38%, and the non-performing rate was reduced by 1.2 percentage points. The research proposes that by strengthening the integration of data elements, upgrading intelligent evaluation models, and improving diversified collaboration mechanisms, we should build a credit risk prevention system that adapts to the digital economy. Research provides a quantitative basis and practical path for financial risk governance in the new era.