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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

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 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa68405
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 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.
Keywords: Farmers, credit risk, hybrid ensemble model, interpretation
College: Faculty of Humanities and Social Sciences
Funders: 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).

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