Risk assessment of financial data based on logistic regression algorithm

Benford's law is a commonly used method to test the quality of financial data, and introducing Benford's law into the logistic regression model for financial risk early warning can increase the number of effective variables representing the quality of financial data and improve the prediction accuracy of the early warning model.This paper applies Benford's law to test the quality of financial data, constructs modified Benford's factor, and combines it with financial variables to establish a Benford-Logistic model for financial risk warning.Taking Chinese A-share listed companies from 2006 to 2023 as samples, the Lasso method is used to screen the explanatory variables and determine the optimal model so as to realize the risk assessment of the financial data, and the validity of the model is verified by taking Company A as the target.The results of the study show that the introduction of the modified Benford quality factor into the logistic regression model can improve the accuracy of the early warning model for the risk of corporate financial data, and the model constructed is effective when applied to Company A. It is in line with the actual situation of Company A. The early warning model is of great significance in the prevention of the financial risk of the enterprise.