Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network

Credit risk prediction for small and medium enterprises (SMEs) has long posed a complex research challenge. Traditional approaches have primarily focused on enterprise-specific variables, but these models often prove inadequate when applied to SMEs with incomplete data. In this innovative study, we...
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Autor*in:	Jiaxing Wang [verfasserIn] Guoquan Liu [verfasserIn] Xiaobo Xu [verfasserIn] Xinjie Xing [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Risk management SME Credit risk Transactional data Graph neural network

Übergeordnetes Werk:	In: Journal of Management Science and Engineering - KeAi Communications Co., Ltd., 2020, 9(2024), 2, Seite 177-192
Übergeordnetes Werk:	volume:9 ; year:2024 ; number:2 ; pages:177-192

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.jmse.2023.11.005

Katalog-ID:	DOAJ095706038

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source	In Journal of Management Science and Engineering 9(2024), 2, Seite 177-192 volume:9 year:2024 number:2 pages:177-192
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callnumber-first	T - Technology
author	Jiaxing Wang
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topic	misc T55.4-60.8 misc Risk management misc SME misc Credit risk misc Transactional data misc Graph neural network misc Industrial engineering. Management engineering
topic_unstemmed	misc T55.4-60.8 misc Risk management misc SME misc Credit risk misc Transactional data misc Graph neural network misc Industrial engineering. Management engineering
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title	Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network
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title_full	Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network
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title_auth	Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network
abstract	Credit risk prediction for small and medium enterprises (SMEs) has long posed a complex research challenge. Traditional approaches have primarily focused on enterprise-specific variables, but these models often prove inadequate when applied to SMEs with incomplete data. In this innovative study, we push the theoretical boundaries by leveraging data from adjacent enterprises to address the issue of data deficiency. Our strategy involves constructing an intricate network that interconnects enterprises based on shared managerial teams and business interactions. Within this network, we propose a novel relational graph attention network (RGAT) algorithm capable of capturing the inherent complexity in its topological information. By doing so, our model enhances financial service providers' ability to predict credit risk even in the face of incomplete data from target SMEs. Empirical experiments conducted using China's SMEs highlight the predictive proficiency and potential economic benefits of our proposed model. Our approach offers a comprehensive and nuanced perspective on credit risk while demonstrating the advantages of incorporating network-wide data in credit risk prediction.
abstractGer	Credit risk prediction for small and medium enterprises (SMEs) has long posed a complex research challenge. Traditional approaches have primarily focused on enterprise-specific variables, but these models often prove inadequate when applied to SMEs with incomplete data. In this innovative study, we push the theoretical boundaries by leveraging data from adjacent enterprises to address the issue of data deficiency. Our strategy involves constructing an intricate network that interconnects enterprises based on shared managerial teams and business interactions. Within this network, we propose a novel relational graph attention network (RGAT) algorithm capable of capturing the inherent complexity in its topological information. By doing so, our model enhances financial service providers' ability to predict credit risk even in the face of incomplete data from target SMEs. Empirical experiments conducted using China's SMEs highlight the predictive proficiency and potential economic benefits of our proposed model. Our approach offers a comprehensive and nuanced perspective on credit risk while demonstrating the advantages of incorporating network-wide data in credit risk prediction.
abstract_unstemmed	Credit risk prediction for small and medium enterprises (SMEs) has long posed a complex research challenge. Traditional approaches have primarily focused on enterprise-specific variables, but these models often prove inadequate when applied to SMEs with incomplete data. In this innovative study, we push the theoretical boundaries by leveraging data from adjacent enterprises to address the issue of data deficiency. Our strategy involves constructing an intricate network that interconnects enterprises based on shared managerial teams and business interactions. Within this network, we propose a novel relational graph attention network (RGAT) algorithm capable of capturing the inherent complexity in its topological information. By doing so, our model enhances financial service providers' ability to predict credit risk even in the face of incomplete data from target SMEs. Empirical experiments conducted using China's SMEs highlight the predictive proficiency and potential economic benefits of our proposed model. Our approach offers a comprehensive and nuanced perspective on credit risk while demonstrating the advantages of incorporating network-wide data in credit risk prediction.
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title_short	Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network
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score	7.399164

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Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network

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