{"type":"ScholarlyArticle","title":"Financial Risk Analysis for SMEs with Graph-based Supply Chain Mining","authors":[{"name":"Shuo Yang","slug":"shuo-yang","given":"Shuo","family":"Yang","affiliation":""},{"name":"Zhiqiang Zhang","slug":"zhiqiang-zhang","given":"Zhiqiang","family":"Zhang","affiliation":""},{"name":"Jun Zhou","slug":"jun-zhou","given":"Jun","family":"Zhou","affiliation":""},{"name":"Yang Wang","slug":"yang-wang","given":"Yang","family":"Wang","affiliation":""},{"name":"Wang Sun","slug":"wang-sun","given":"Wang","family":"Sun","affiliation":""},{"name":"Xingyu Zhong","slug":"xingyu-zhong","given":"Xingyu","family":"Zhong","affiliation":""},{"name":"Yanming Fang","slug":"yanming-fang","given":"Yanming","family":"Fang","affiliation":""},{"name":"Quan Yu","slug":"quan-yu","given":"Quan","family":"Yu","affiliation":""},{"name":"Yuan Qi","slug":"yuan-qi","given":"Yuan","family":"Qi","affiliation":""}],"abstract":"Small and Medium-sized Enterprises (SMEs) are playing a vital role in the modern economy. Recent years, financial risk analysis for SMEs attracts lots of attentions from financial institutions.\r\nHowever, the financial risk analysis for SMEs usually suffers data deficiency problem, especially for the mobile financial institutions which seldom collect credit-related data directly from SMEs.\r\nFortunately, although credit-related information of SMEs is hard to be acquired sufficiently, the interactive relationships between SMEs, which may contain valuable information of financial risk, is usually available for the mobile financial institutions.\r\nFinding out credit-related relationship of SME from massive interactions helps comprehensively model the SMEs thus improve the performance of financial risk analysis.\r\n\r\nIn this paper, tackling the data deficiency problem of financial risk analysis for SMEs, we propose an innovative financial risk analysis framework with graph-based supply chain mining.\r\nSpecifically, to capture the credit-related topology structural and temporal variation information of SMEs, we design and employ a novel spatial-temporal aware graph neural network,  to mine supply chain relationship on a SME graph, and then analysis the credit risk based on the mined supply chain graph.\r\nExperimental results on real-world financial datasets prove the effectiveness of our proposal for financial risk analysis for SMEs.","doi":"10.24963/ijcai.2020/643","url":"https://ijcai.org/proceedings/2020/643","pdf":"https://ijcai.org/proceedings/2020/0643.pdf","markdown":"https://ijcai.org/proceedings/2020/643.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/643","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special Track on AI in FinTech","track":"Special Track on AI in FinTech","number":643,"pages":{"first":4661,"last":4667},"keywords":["AI for risk and security: AI for financial risk factors and prediction","AI for banking: AI for credit loan","AI for lending: General"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24461/file_24461.m3u8","length_sec":313,"provider":"hls"}],"references":[{"position":1,"raw":"[Beck et al., 2011] Thorsten Beck, Asli Demirgüc ¸-Kunt, and María Soledad Martínez Pería. 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