Growth potential of machine learning in credit risk predicting of farmers in the industry 4.0 era
Nana Chai,
Mohammad Zoynul Abedin,
Xiaoling Wang
et al.
Abstract:This paper aims to design a model framework for farmer credit risk assessment based on machine learning. It reduces the degree of credit risk misjudgement caused by the weak correlation between evaluation indicators and default status and imbalanced data. Based on the empirical analysis of 8624 farmers' data from a commercial bank in China, the average rank of the OPSO‐GINI‐FS model designed from the feature dimension is 1.29, which is higher than that of the OPSO‐GINI‐IS model designed from the indicator dime… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.