Application of Artificial Intelligence Technology Optimized by Deep Learning to Rural Financial Development and Rural Governance

Hongwei Hou, Kunzhi Tang, Xiaoqian Liu, Yue Zhou

OAI: oai:igi-global.com:289220 • DOI: 10.4018/JGIM.289220

The aim of this article is to promote the development of rural finance and the further informatization of rural banks. Based on DL (deep learning) and artificial intelligence technology, data pre-processing and feature selection are conducted on the customer information of rural banks in a certain region, including the historical deposit and loan, transaction record, and credit information. Besides, four DL models are proposed with a precision of more than 87% by test to improve the simulation effect and explore the application of DL. The BLSTM-CNN (Bi-directional Long Short-Term Memory-Convolutional Neural Network) model with a precision of 95.8%, which integrates RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) in parallel, solves the shortcomings of RNN and CNN separately. The research result can provide a more reasonable prediction model for rural banks, and ideas for the development of rural informatization and promoting rural governance.

Global Information Technology Computer Science and Information Technology IT Research and Theory