Application of Data Mining Combined with K-means Clustering Algorithm in Enterprises' Risk Audit

Abstract

The financial risk management mechanism of enterprises can be more complete through exploration in the application effect of data mining technology combined with K-means clustering algorithm in enterprise risk audit. Hence, K-means clustering algorithm is introduced to study the paperless status of electronic payment in the trading process of e-commerce enterprises. Additionally, a risk audit model of e-commerce enterprises is implemented based on K-means algorithm combined with Random Forest Light Gradient Boosting Machine (RF-LightGBM). In this model, the actual operation process of data preparation, data preprocessing, model construction, model application and evaluation are implemented to study the payment flow in the transaction process of e-commerce enterprises by using big data analysis technology. Eventually, the performance of the model is evaluated by simulation. The results show that, compared with the models and algorithms proposed by scholars in other related fields, the classification accuracy of the model proposed here reaches 95.46 %. Simultaneously, the data message delivery rate of the model algorithm is basically stable at about 81.54 %, and the data message leakage rate, packet loss rate and average delay are lower than those of other models and algorithms. Therefore, under the premise of ensuring the prediction accuracy, the audit model of e-commerce enterprises can also achieve high data transmission security performance, which can provide experimental basis for the safety improvement and risk control of the audit process in e-commerce enterprises.