Enhancing Corporate Financial Transparency and Performance Assessment through Big Data and Machine Learning

The higher the corporate financial transparency, the more it can reduce the information asymmetry, which can enhance the market trust and improve the corporate performance.In order to improve corporate financial transparency, the study constructs a financial fraud identification model by improving the machine learning model based on XGBoost algorithm from the financial fraud factors.Based on the XGBoost algorithm, the model integrates the decision rules through the weighted fusion method to generate a new decision tree to determine the financial fraud.In order to improve the ability of enterprise performance assessment, the baryon support vector machine method is used to classify the performance of enterprise employees, and the nonlinear baryon support vector machine is used to establish the enterprise performance assessment model.In the process of verifying the effect of the two models, text indicators are extracted using big data technology to provide a rich feature set for the financial fraud identification model.The data from ERP, CRM and other systems are integrated to provide a comprehensive and high-quality data set for the enterprise performance assessment model.After empirical analysis, the combination of big data and machine learning can improve the effect of financial fraud identification, and then effectively improve the transparency of corporate finance.The enterprise performance evaluation model provides a scientific and efficient quantitative evaluation tool for enterprise managers, and effectively improves the enterprise performance evaluation capability.