基于大数据信息赋能的企业业财融合风险评价研究

Research on the Risk Evaluation of Enterprise Industry and Finance Integration Based on Big Data Information Empowerment

研究旨在探讨大数据信息赋能背景下企业财务与风险融合挑战。并建立了风险评估体系。通过指标选取和模型概述,研究设计了神经网络判别模型处理分类问题。数据获取方面。通过问卷训查收集1469份有效问卷,进行信度与效度检验。结果与分析部分描述了样本特征、线性回归结果和神经网络模型分析。描述性统计显示不同内外部风险因素的影响情况,线性回归表明技术创新和社会责任等因素对业财融合风险具有显著影响。神经网络模型分析显示在训练和验证集上表现良好,但测试集存在拟合不足问题。混淆矩阵和ROC曲线展示了模型的分类能力和性能。基于此分析结果,企业应注重数据整合与分析、风险评估与管理、智能决策支持等方面来有效应对财务与风险融合挑战。这些举措将有助于企业更好地理解市场趋势、客户需求以及潜在风险因素,并及时做出相应决策,保持竞争优势。

This study aims to explore the challenges of integrating corporate finance and risk within the context of big data information empowerment,and establishes a risk assessment system.Through indicator selection,and model overview,the study designed a neural network discriminant model to address the classification problem.For data acquisition,researchers collected 1,469 valid questionnaires through surveys for reliability and validity tests.The results and analysis section describes the sample characteristics,linear regression results,and neural network model analysis.Descriptive statistics show the influence of various internal and external risk factors,while linear regression indicates that factors such as technological innovation and social responsibility significantly affect industry-finance integration risk.The neural network model analysis demonstrates good performance on the training and validation sets,but the test set suffers from underfitting.The confusion matrix and ROC curves illustrate the model's classification capabilities and performance.Based on the findings of this analysis,organizations should focus on data integration and analysis,risk assessment and management,and intelligent decision support to effectively tackle financial and risk convergence.These initiatives will help enterprises better understanding market trends,customer needs,and potential risk factors,enabling timely decisions to maintain competitive advantages.