Application of Multiobjective Particle Swarm Optimization in Rural Credit System

In China’s rural credit system, the problem of credit constraints is prominent. Due to the imperfect credit market, a large number of rural residents have credit constraints. Rural credit constraint is a serious problem restricting China’s rural economic development. Aimed at solving the rural credi...

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Vydané v:Journal of sensors Ročník 2021; číslo 1
Hlavný autor: Xue, Wuxia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Hindawi 23.12.2021
John Wiley & Sons, Inc
Predmet:
Algorithms
Big Data
Collateral
Consumption
Credit risk
Decision analysis
Decision making
Decomposition
Economic development
Expenditures
Financial services
Internal customers
Loans
Machine learning
Multiple objective analysis
Neural networks
Optimization algorithms
Particle swarm optimization
Personal information
Regulation of financial institutions
Risk assessment
Rural areas
China
ISSN:1687-725X, 1687-7268
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Shrnutí:In China’s rural credit system, the problem of credit constraints is prominent. Due to the imperfect credit market, a large number of rural residents have credit constraints. Rural credit constraint is a serious problem restricting China’s rural economic development. Aimed at solving the rural credit constraints, this paper makes an optimization analysis on the rural credit system and loan decision-making. To more reasonably evaluate customers’ borrowing ability, the credit risk based on farmers’ data on the big data platform is evaluated in this paper. The stacked denoising autoencoder network is improved by adopting the deep learning framework to improve the accuracy of credit evaluation. For improving the loan decision-making ability of rural credit system, a loan optimization strategy based on multiobjective particle swarm optimization algorithm is proposed. The simulation results show that the optimization ability, speed, and stability of the proposed algorithm have achieved good results in dealing with the loan portfolio decision-making problem.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1687-725X
1687-7268
DOI:10.1155/2021/3468479