Heterogeneous Graph Attention Network for Small and Medium-Sized Enterprises Bankruptcy Prediction

Zheng, Y; Lee, VCS; Wu, Z; Pan, S

Heterogeneous Graph Attention Network for Small and Medium-Sized Enterprises Bankruptcy Prediction

Zheng, Y Lee, VCS Wu, Z Pan, S

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, 12712 LNAI, pp. 140-151
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Zheng, Y
dc.contributor.author	Lee, VCS
dc.contributor.author	Wu, Z
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X
dc.contributor.editor	Karlapalem, K
dc.contributor.editor	Cheng, H
dc.contributor.editor	Ramakrishnan, N
dc.contributor.editor	Agrawal, RK
dc.contributor.editor	Reddy, PK
dc.contributor.editor	Srivastava, J
dc.contributor.editor	Chakraborty, T
dc.date	2021-05-11
dc.date.accessioned	2022-06-29T08:27:05Z
dc.date.available	2022-06-29T08:27:05Z
dc.date.issued	2021-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, 12712 LNAI, pp. 140-151
dc.identifier.isbn	9783030757618
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/158466
dc.description.abstract	Credit assessment for Small and Medium-sized Enterprises (SMEs) is of great interest to financial institutions such as commercial banks and Peer-to-Peer lending platforms. Effective credit rating modeling can help them make loan-granted decisions while limiting their risk exposure. Despite a substantial amount of research being conducted in this domain, there are three existing issues. Firstly, many of them are mainly developed based on financial statements, which usually are not publicly-accessible for SMEs. Secondly, they always neglect the rich relational information embodied in financial networks. Finally, existing graph-neural-network-based (GNN) approaches for credit assessment are only applicable to homogeneous networks. To address these issues, we propose a heterogeneous-attention-network-based model (HAT) to facilitate SMEs bankruptcy prediction using publicly-accessible data. Specifically, our model has two major components: a heterogeneous neighborhood encoding layer and a triple attention output layer. While the first layer can encapsulate target nodes’ heterogeneous neighborhood information to address the graph heterogeneity, the latter can generate the prediction by considering the importance of different metapath-based neighbors, metapaths, and networks. Extensive experiments in a real-world dataset demonstrate the effectiveness of our model compared with baselines.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartof	Pacific-Asia Conference on Knowledge Discovery and Data Mining
dc.relation.ispartofseries	Lecture Notes in Artificial Intelligence
dc.relation.isbasedon	10.1007/978-3-030-75762-5_12
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Heterogeneous Graph Attention Network for Small and Medium-Sized Enterprises Bankruptcy Prediction
dc.type	Conference Proceeding
utslib.citation.volume	12712 LNAI
utslib.location.activity	Virtual Event
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-29T08:27:02Z
pubs.finish-date	2021-05-14
pubs.place-of-publication	Cham, Switzerland
pubs.publication-status	Published
pubs.start-date	2021-05-11
pubs.volume	12712 LNAI
dc.location	Cham, Switzerland

Abstract:

Credit assessment for Small and Medium-sized Enterprises (SMEs) is of great interest to financial institutions such as commercial banks and Peer-to-Peer lending platforms. Effective credit rating modeling can help them make loan-granted decisions while limiting their risk exposure. Despite a substantial amount of research being conducted in this domain, there are three existing issues. Firstly, many of them are mainly developed based on financial statements, which usually are not publicly-accessible for SMEs. Secondly, they always neglect the rich relational information embodied in financial networks. Finally, existing graph-neural-network-based (GNN) approaches for credit assessment are only applicable to homogeneous networks. To address these issues, we propose a heterogeneous-attention-network-based model (HAT) to facilitate SMEs bankruptcy prediction using publicly-accessible data. Specifically, our model has two major components: a heterogeneous neighborhood encoding layer and a triple attention output layer. While the first layer can encapsulate target nodes’ heterogeneous neighborhood information to address the graph heterogeneity, the latter can generate the prediction by considering the importance of different metapath-based neighbors, metapaths, and networks. Extensive experiments in a real-world dataset demonstrate the effectiveness of our model compared with baselines.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/158466