大数据分析对企业信用风险评估的影响——基于银行信贷数据的实证研究

The Impact of Big Data Analysis on Enterprise Credit Risk Assessment—An Empirical Study Based on Bank Loan Data

摘要: 数字经济时代，金融科技快速发展推动企业信用风险评估模式的深刻变革。银行信贷数据蕴含丰富的风险信息，通过大数据分析技术，可以突破传统风险评估方法的局限性。该研究以某大型商业银行信贷数据为基础，构建多维度数据分析模型，深入挖掘企业信用风险的内在机理。运用描述性统计与关联性分析以及概率模型，系统性地探索大数据分析对信用风险识别、预测与管理的实践价值。实证分析表明，大数据分析显著提升了风险评估的准确性与效率，能够精准刻画企业信用风险特征，为金融机构信贷决策提供有力支撑。研究成果不仅丰富了企业信用风险评估的理论内涵，还为金融机构运用大数据技术优化风险管理提供了实践指导。

Abstract: In the digital economy era, the rapid development of financial technology drives profound transformation in enterprise credit risk assessment models. Bank loan data contains rich risk information, and through big data analysis technology, traditional risk assessment methods’ limitations can be broken through. This study, based on a large commercial bank’s loan data, constructs a multi-dimensional data analysis model to deeply explore the intrinsic mechanism of enterprise credit risk. By employing descriptive statistics, correlation analysis, and probability models, the study systematically explores the practical value of big data analysis in credit risk identification, prediction, and management. Empirical analysis demonstrates that big data analysis significantly improves risk assessment accuracy and efficiency, enabling precise portrayal of enterprise credit risk characteristics and providing strong support for financial institutions’ lending decisions. The research results not only enrich the theoretical connotation of enterprise credit risk assessment but also offer practical guidance for financial institutions to optimize risk management using big data technology.