Transformed Graph Attention for Credit Rating

Liu, CZ; Xiang, S; Cheng, D; Liu, J; Zhang, Y; Qin, L

Transformed Graph Attention for Credit Rating

Liu, CZ Xiang, S

Cheng, D Liu, J Zhang, Y Qin, L

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA), 2023, 00, pp. 1011-1016
Issue Date:: 2023-09-11

Closed Access

	Filename	Description	Size
	Transformed_Graph_Attention_for_Credit_Rating.pdf	Published version	10.35 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Liu, CZ
dc.contributor.author	Xiang, S https://orcid.org/0000-0002-1454-8240
dc.contributor.author	Cheng, D
dc.contributor.author	Liu, J
dc.contributor.author	Zhang, Y
dc.contributor.author	Qin, L
dc.date	2023-08-18
dc.date.accessioned	2024-05-03T00:47:15Z
dc.date.available	2024-05-03T00:47:15Z
dc.date.issued	2023-09-11
dc.identifier.citation	2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA), 2023, 00, pp. 1011-1016
dc.identifier.isbn	9798350312201
dc.identifier.uri	http://hdl.handle.net/10453/178563
dc.description.abstract	In banking finance and lending credit rating is an important tool to measure creditor and debt reliability and prevent systemic risk among banks Intrinsic features are key factors in risk control decisions Decision models built with these intrinsic features are often used to assess risk classification and asset safety but there lacks research on credit prediction for bank rating outlooks with financial correlation which reflects the structured pattern of systemic risk and contagion Although correlations among financial entities are valuable for rating outlook it is hard to integrate such interactions into feature based solutions Aiming at the issues above we propose a hyper feature mapping operator based on an attention based knowledge representation to build a knowledge system for the credit rating prediction of the interbank to represent the knowledge of credit rating Combined with the interrelated behavior graph of each entity a hyper context graph attention with intrinsic properties assets capabilities buffers are represented as knowledge for rating prediction to extract the context representation helping understand the interaction of risk between intrinsic properties and entity to entity Through the experimental comparison and evaluation based on 10 years of actual interbank data the results show that the proposed model is effective with higher performance compared with the popular rating prediction methods currently used in the industry
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA)
dc.relation.ispartof	2023 IEEE 18th Conference on Industrial Electronics and Applications
dc.relation.isbasedon	10.1109/iciea58696.2023.10241546
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Transformed Graph Attention for Credit Rating
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Ningbo, China
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-03T00:47:10Z
pubs.finish-date	2023-08-22
pubs.publication-status	Published
pubs.start-date	2023-08-18
pubs.volume	00

Abstract:

In banking finance and lending credit rating is an important tool to measure creditor and debt reliability and prevent systemic risk among banks Intrinsic features are key factors in risk control decisions Decision models built with these intrinsic features are often used to assess risk classification and asset safety but there lacks research on credit prediction for bank rating outlooks with financial correlation which reflects the structured pattern of systemic risk and contagion Although correlations among financial entities are valuable for rating outlook it is hard to integrate such interactions into feature based solutions Aiming at the issues above we propose a hyper feature mapping operator based on an attention based knowledge representation to build a knowledge system for the credit rating prediction of the interbank to represent the knowledge of credit rating Combined with the interrelated behavior graph of each entity a hyper context graph attention with intrinsic properties assets capabilities buffers are represented as knowledge for rating prediction to extract the context representation helping understand the interaction of risk between intrinsic properties and entity to entity Through the experimental comparison and evaluation based on 10 years of actual interbank data the results show that the proposed model is effective with higher performance compared with the popular rating prediction methods currently used in the industry

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

http://hdl.handle.net/10453/178563