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Rural micro-credit model design and credit risk assessment via improved LSTM algorithm

Rural microcredit plays an important role in promoting rural economic development and increasing farmers’ income. However, traditional credit risk assessment models may have insufficient adaptability in rural areas. This study is based on the improved Long Short Term Memory (LSTM) algorithm using se...

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Detalles Bibliográficos
Autores principales: Gao, Xia, Yang, Xiaoqian, Zhao, Yuchen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557499/
https://www.ncbi.nlm.nih.gov/pubmed/37810351
http://dx.doi.org/10.7717/peerj-cs.1588
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author Gao, Xia
Yang, Xiaoqian
Zhao, Yuchen
author_facet Gao, Xia
Yang, Xiaoqian
Zhao, Yuchen
author_sort Gao, Xia
collection PubMed
description Rural microcredit plays an important role in promoting rural economic development and increasing farmers’ income. However, traditional credit risk assessment models may have insufficient adaptability in rural areas. This study is based on the improved Long Short Term Memory (LSTM) algorithm using self organizing method, aiming to design an optimized evaluation model for rural microcredit risk. The improved LSTM algorithm can better capture the long-term dependence between the borrower’s historical behavior and risk factors with its advantages in sequential data modeling. The experimental results show that the rural microcredit risk assessment model based on the self organizing LSTM algorithm has higher accuracy and stability compared to traditional models, and can effectively control credit default risk, providing more comprehensive risk management support for financial institutions. In addition, the model also has real-time monitoring and warning functions, which helps financial institutions adjust their decisions in a timely manner and reduce credit losses. The practical application of this study is expected to promote the stable development of rural economy and the advancement of financial technology. However, future work needs to further validate the practical application effectiveness and interpretability of the model, taking into account the special circumstances of different rural areas, in order to achieve sustainable application of the model in the rural microcredit market.
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spelling pubmed-105574992023-10-07 Rural micro-credit model design and credit risk assessment via improved LSTM algorithm Gao, Xia Yang, Xiaoqian Zhao, Yuchen PeerJ Comput Sci Algorithms and Analysis of Algorithms Rural microcredit plays an important role in promoting rural economic development and increasing farmers’ income. However, traditional credit risk assessment models may have insufficient adaptability in rural areas. This study is based on the improved Long Short Term Memory (LSTM) algorithm using self organizing method, aiming to design an optimized evaluation model for rural microcredit risk. The improved LSTM algorithm can better capture the long-term dependence between the borrower’s historical behavior and risk factors with its advantages in sequential data modeling. The experimental results show that the rural microcredit risk assessment model based on the self organizing LSTM algorithm has higher accuracy and stability compared to traditional models, and can effectively control credit default risk, providing more comprehensive risk management support for financial institutions. In addition, the model also has real-time monitoring and warning functions, which helps financial institutions adjust their decisions in a timely manner and reduce credit losses. The practical application of this study is expected to promote the stable development of rural economy and the advancement of financial technology. However, future work needs to further validate the practical application effectiveness and interpretability of the model, taking into account the special circumstances of different rural areas, in order to achieve sustainable application of the model in the rural microcredit market. PeerJ Inc. 2023-09-26 /pmc/articles/PMC10557499/ /pubmed/37810351 http://dx.doi.org/10.7717/peerj-cs.1588 Text en ©2023 Gao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Gao, Xia
Yang, Xiaoqian
Zhao, Yuchen
Rural micro-credit model design and credit risk assessment via improved LSTM algorithm
title Rural micro-credit model design and credit risk assessment via improved LSTM algorithm
title_full Rural micro-credit model design and credit risk assessment via improved LSTM algorithm
title_fullStr Rural micro-credit model design and credit risk assessment via improved LSTM algorithm
title_full_unstemmed Rural micro-credit model design and credit risk assessment via improved LSTM algorithm
title_short Rural micro-credit model design and credit risk assessment via improved LSTM algorithm
title_sort rural micro-credit model design and credit risk assessment via improved lstm algorithm
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557499/
https://www.ncbi.nlm.nih.gov/pubmed/37810351
http://dx.doi.org/10.7717/peerj-cs.1588
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