LSTM Neural Network-Based Credit Prediction Method for Food Companies

Abstract

As information technology expands across industries in the age of deep learning, companies face new changes in their credit assessment methods. One of the difficulties in financing food enterprises stems from the complexity of investment in reviewing enterprises’ credit. Therefore, this paper proposes a deep learning-based credit prediction and evaluation model for food enterprises, which performs well on the dataset and achieves 85.73% and 88.56% accuracy in verifying the performance and default test samples, respectively. In addition, the model was confirmed to have good robustness through ablation experiments. Finally, the paper concludes with relevant recommendations for food companies based on the study’s findings, offering new methods to improve their corporate credit assessment.