research-article

Loan Default Analysis with Multiplex Graph Learning

Authors:

Zhiqiang Zhang,

Yuan QiAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 2525 - 2532

https://doi.org/10.1145/3340531.3412724

Published: 19 October 2020 Publication History

Abstract

Aiming to effectively distinguish loan default in the Mobile Credit Payment Service, industrial efforts mainly attempt to employ conventional classifier with complicated feature engineer for prediction. However, these solutions fail to exploit multiplex relations existed in the financial scenarios and ignore the key intrinsic properties of the loan default detection, i.e., communicability, complementation and induction. To address these issues, we develop a novel attributed multiplex graph based loan default detection approach for effectively integrating multiplex relations in financial scenarios. Considering the complexity of financial scenario, an Attributed Multiplex Graph (AMG) is proposed to jointly model various relations and objects as well as the rich attributes on nodes and edges. We elaborately design relation-specific receptive layers equipped with adaptive breadth function to incorporate important information derived from local structure in each aspect of AMG and stack multiple propagation layer to explore the high-order connectivity information. Furthermore, a relation-specific attention mechanism is adopted to emphasize relevant information during end-to-end training. Extensive experiments conducted on the large-scale real- world dataset verify the effectiveness of the proposed model com- pared with state of arts. Moreover, AMG-DP has also achieved a performance improvement of 9.37% on KS metric in recent months after successful deployment in the Alipay APP.

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Cited By

Chen LMa SLi CYang YWei WCui R(2024)A spatial–temporal graph-based AI model for truck loan default prediction using large-scale GPS trajectory dataTransportation Research Part E: Logistics and Transportation Review10.1016/j.tre.2024.103445183(103445)Online publication date: Mar-2024
https://doi.org/10.1016/j.tre.2024.103445
Li ZChen YWang XYao LXu G(2024)Multi-view GCN for loan default risk predictionNeural Computing and Applications10.1007/s00521-024-09695-x36:20(12149-12162)Online publication date: 19-Apr-2024
https://doi.org/10.1007/s00521-024-09695-x
Zhou JLi MYu LLi LWu F(2023)A Practical Rule Learning Framework for Risk ManagementCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3584644(442-446)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3584644
Show More Cited By

Index Terms

Loan Default Analysis with Multiplex Graph Learning

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

Copyright © 2020 ACM.

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Published: 19 October 2020

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CIKM '20: The 29th ACM International Conference on Information and Knowledge Management

October 19 - 23, 2020

Virtual Event, Ireland

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Cited By

Chen LMa SLi CYang YWei WCui R(2024)A spatial–temporal graph-based AI model for truck loan default prediction using large-scale GPS trajectory dataTransportation Research Part E: Logistics and Transportation Review10.1016/j.tre.2024.103445183(103445)Online publication date: Mar-2024
https://doi.org/10.1016/j.tre.2024.103445
Li ZChen YWang XYao LXu G(2024)Multi-view GCN for loan default risk predictionNeural Computing and Applications10.1007/s00521-024-09695-x36:20(12149-12162)Online publication date: 19-Apr-2024
https://doi.org/10.1007/s00521-024-09695-x
Zhou JLi MYu LLi LWu F(2023)A Practical Rule Learning Framework for Risk ManagementCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3584644(442-446)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3584644
Chen JLiu JWu ZZhao SLi QWu J(2023)Financial Loan Overdue Risk Detection via Meta-path-based Graph Neural Network2023 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS46773.2023.10181590(1-5)Online publication date: 21-May-2023
https://doi.org/10.1109/ISCAS46773.2023.10181590
Kang YChen LJia NWei WDeng JQian H(2022)A CWGAN-GP-based multi-task learning model for consumer credit scoringExpert Systems with Applications10.1016/j.eswa.2022.117650206(117650)Online publication date: Nov-2022
https://doi.org/10.1016/j.eswa.2022.117650

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