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Farmers' credit risk evaluation with an explainable hybrid ensemble approach: A closer look in microfinance

Nana Chai, Mohammad Abedin Orcid Logo, Lian Yang, Baofeng Shi

Pacific-Basin Finance Journal, Volume: 89, Start page: 102612

Swansea University Author: Mohammad Abedin Orcid Logo

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DOI (Published version): 10.1016/j.pacfin.2024.102612

Abstract

Artificial intelligence stimulates the vitality of microcredit by reshaping credit risk evaluation models, especially targeting the group of farmers. Therefore, the paper aims to establish a new interpretable hybrid ensemble model for evaluating the credit risk of microfinance for farmers, which is...

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Published in: Pacific-Basin Finance Journal
ISSN: 0927-538X 1879-0585
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa68405
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spelling 2025-02-04T13:10:09.3356718 v2 68405 2024-12-02 Farmers' credit risk evaluation with an explainable hybrid ensemble approach: A closer look in microfinance 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2024-12-02 CBAE Artificial intelligence stimulates the vitality of microcredit by reshaping credit risk evaluation models, especially targeting the group of farmers. Therefore, the paper aims to establish a new interpretable hybrid ensemble model for evaluating the credit risk of microfinance for farmers, which is called ADASYN (Adaptive Synthetic Sampling)-LCE (Local Cascade Ensemble)-Shapash. It integrates the advantages of three ensemble models: bagging, boosting, and local cascading, including reducing model variance, reducing model bias, and simplifying complex problems by learning different parts of the training data. And it alleviates the problem of low generalization performance of traditional ensemble models caused by imbalanced loan data of farmers. Through the empirical analysis of the data of farmers' loans of China poverty alleviation agency “CHONGHO BRIDGE”, it is found that its average rank is 2.1, which is better than other integrated models in the credit risk evaluation of farmers' microfinance. Finally, the global and local interpretation of our model is preliminarily explored. Journal Article Pacific-Basin Finance Journal 89 102612 Elsevier BV 0927-538X 1879-0585 Farmers; Credit risk; Hybrid ensemble model; Interpretation 1 2 2025 2025-02-01 10.1016/j.pacfin.2024.102612 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) This paper was supported by the Major Program of the National Social Science Foundation of China (Grant No. 23&ZD175), the National Natural Science Foundation of China (Grant Nos. 72173096); the Postdoctoral Fellowship Program (Grade B) of China Postdoctoral Science Foundation (Grant No. GZB20240546); the Shandong Provincial Natural Science Foundation (Grants No. ZR2024QG002). 2025-02-04T13:10:09.3356718 2024-12-02T09:35:46.4185134 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Nana Chai 1 Mohammad Abedin 0000-0002-4688-0619 2 Lian Yang 3 Baofeng Shi 4 68405__33492__4ba89c437fea42e18d5c97c8891b7fc7.pdf 68405.VOR.pdf 2025-02-04T13:08:02.4355702 Output 4841664 application/pdf Version of Record true © 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons CC-BY license. true eng http://creativecommons.org/licenses/by/4.0/
title Farmers' credit risk evaluation with an explainable hybrid ensemble approach: A closer look in microfinance
spellingShingle Farmers' credit risk evaluation with an explainable hybrid ensemble approach: A closer look in microfinance
Mohammad Abedin
title_short Farmers' credit risk evaluation with an explainable hybrid ensemble approach: A closer look in microfinance
title_full Farmers' credit risk evaluation with an explainable hybrid ensemble approach: A closer look in microfinance
title_fullStr Farmers' credit risk evaluation with an explainable hybrid ensemble approach: A closer look in microfinance
title_full_unstemmed Farmers' credit risk evaluation with an explainable hybrid ensemble approach: A closer look in microfinance
title_sort Farmers' credit risk evaluation with an explainable hybrid ensemble approach: A closer look in microfinance
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Nana Chai
Mohammad Abedin
Lian Yang
Baofeng Shi
format Journal article
container_title Pacific-Basin Finance Journal
container_volume 89
container_start_page 102612
publishDate 2025
institution Swansea University
issn 0927-538X
1879-0585
doi_str_mv 10.1016/j.pacfin.2024.102612
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 1
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description Artificial intelligence stimulates the vitality of microcredit by reshaping credit risk evaluation models, especially targeting the group of farmers. Therefore, the paper aims to establish a new interpretable hybrid ensemble model for evaluating the credit risk of microfinance for farmers, which is called ADASYN (Adaptive Synthetic Sampling)-LCE (Local Cascade Ensemble)-Shapash. It integrates the advantages of three ensemble models: bagging, boosting, and local cascading, including reducing model variance, reducing model bias, and simplifying complex problems by learning different parts of the training data. And it alleviates the problem of low generalization performance of traditional ensemble models caused by imbalanced loan data of farmers. Through the empirical analysis of the data of farmers' loans of China poverty alleviation agency “CHONGHO BRIDGE”, it is found that its average rank is 2.1, which is better than other integrated models in the credit risk evaluation of farmers' microfinance. Finally, the global and local interpretation of our model is preliminarily explored.
published_date 2025-02-01T08:39:15Z
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score 11.059721

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