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

Abstract

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.

Peer-to-peer lending platform risk analysis: an early warning model based on multi-dimensional information

“…Given the rapid development of advanced technology such as machine learning (Cheng et al. , 2021; Srivastava and Eachempati, 2021), deep learning (Du and Shu, 2022; Hou et al. , 2022; Wu et al.…”

“…This paper combined econometric theory and text mining technology to achieve the risk knowledge acquisition of the P2P lending platform. Given the rapid development of advanced technology such as machine learning (Cheng et al, 2021;Srivastava and Eachempati, 2021), deep learning (Du and Shu, 2022;Hou et al, 2022;Wu et al, 2022), and the internet of Things (Almomani et al, 2021;Huang et al, 2021;Peng et al, 2021), future research should employ the latest technology to obtain more accurate results.…”

A parallel integrated learning technique of improved particle swarm optimization and BP neural network and its application

“…Swarm intelligence algorithm serves as a useful model for further optimizing and enhancing the conventional machine learning algorithm. Machine learning, as the most widely used technology in the twenty-first century, has been favored by more and more scholars and has been employed in fields such as medical 20 , education 21 , industry 22 , finance 23 and so on. For example, multilayer Long Short Term Memory (LSTM) networks are used for demand forecasting to address the high volatility of demand data 24 ; Random Forest (RF) and Support Vector Machines (SVM) are also used for predictive mapping of aquatic ecosystems 25 ; The dissolved oxygen in urban rivers is predicted and analyzed by Extreme Learning Machine (ELM) and Artificial Neural Network (ANN) 26 ; BP Neural Network (BPNN) is used to establish the evaluation model of software enterprise risk 27 .…”

Research on the Influencing Factors and Countermeasures of Cultivating Financial Innovation Talents under the Background of Big Data

“…Therefore, to strengthen the research on financial technology, strengthen the financial technology, and strengthen the financial industry. Therefore, it is especially urgent and necessary for financial institutions themselves to strengthen their research of financial technology and their thinking on the concept of financial technology [6][7]. These new trends and new changes have profoundly affected the talent training mode of finance majors in colleges and universities [8].…”