Research on early warning of agricultural credit and guarantee risk based on deep learning

• Published in: Neural computing & applications Vol. 34; no. 9; pp. 6673 - 6682
• Main Authors: Zhang, Chao, Wang, Zhenyu, Lv, Jie
• Format: Journal Article
• Language: English
• Published: London Springer London 01-05-2022; Springer Nature B.V
• Subjects: Agricultural lending; Algorithms; Artificial Intelligence; Belief networks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Deep learning; Feature extraction; Image Processing and Computer Vision; Machine learning; Model accuracy; Probability and Statistics in Computer Science; Risk; S.I on NC for Industry 4.0; Special Issue on Neural Computing Challenges and Applications for Industry 4.0; Training; Deep learning; Agricultural credit guarantee risk; Agricultural industry differentiation
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06114-3

Under the impact of agricultural industry differentiation, traditional financial risk model cannot forewarn the guarantee risk of agricultural credit with effectively. This paper proposes an early warning algorithm of agricultural credit and guarantee risk that can effectively overcome the interference of external factors. Using deep learning network, the risk algorithm of agricultural credit and guarantee was built and it could change the deep belief network into supervised learning. To train for an optimal model, two new hidden layers are added to extract image feature vectors, as well as a Softmax classifier. The model is trained and evaluated by the usage of the risk data set of L province from 2017 to 2019, reinforcing the pre-training network and data to deal with the issue of overfitting in training. The results show that the accuracy of the model reaches 92.56%, when the training sample proportion is 90%, with all the 13 factors in the test set taken as input. It shows that the training of the model worked well and that it can effectively predict the risk of agricultural credit and guarantee.