Enterprise Credit Security Prediction and Evaluation Based on Multimodel Fusion

Abstract

Based on the industry data and enterprise data from tens of thousands of small and medium-sized enterprises, a deep learning and machine learning model of credit prediction is constructed through the division of data sets, processing, and integration of models. At first, with the help of two characteristic selection methods, several subsets separated from the dataset are analyzed based on convolutional neural network as coarse prediction. Then, combined with the tree model, the precise prediction is further made for the enterprise credit evaluation. Finally, the model fusion is carried out to obtain high-precision results. In the simulation experiment, this paper takes a data set of 14,366 small and medium-sized enterprise credit evaluations as the analysis samples to verify the results. The accuracy of the model is 97%, which is far more than 93% of single model with metadata set.

The Evaluation on the Credit Risk of Enterprises with the CNN-LSTM-ATT Model

“…Based on the enterprise's behavior data, the proposed model used in this paper integrates CNN and LSTM to evaluate the credit risk of enterprises. LSTM could act in cooperation for the information back and forth in the long sequence, which has the better performance than the general recurrent neural network in the long sequence training [ 31 , 32 ]. CNN could extract vertical and horizontal features.…”

Retracted: Enterprise Credit Security Prediction and Evaluation Based on Multimodel Fusion

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”