{"type":"ScholarlyArticle","title":"Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation","authors":[{"name":"Dawei Cheng","slug":"dawei-cheng","given":"Dawei","family":"Cheng","affiliation":"Department of Computer Science and Engineering , Shanghai Jiao Tong University , Shanghai , China"},{"name":"Yi Tu","slug":"yi-tu","given":"Yi","family":"Tu","affiliation":"Department of Computer Science and Engineering , Shanghai Jiao Tong University , Shanghai , China"},{"name":"Zhenwei Ma","slug":"zhenwei-ma","given":"Zhenwei","family":"Ma","affiliation":"School of Mathematical Science , Shanghai Jiao Tong University , Shanghai , China"},{"name":"Zhibin Niu","slug":"zhibin-niu","given":"Zhibin","family":"Niu","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Liqing Zhang","slug":"liqing-zhang","given":"Liqing","family":"Zhang","affiliation":"Department of Computer Science and Engineering , Shanghai Jiao Tong University , Shanghai , China"}],"abstract":"Assessing and predicting the default risk of networked-guarantee loans is critical for the commercial banks and financial regulatory authorities. The guarantee relationships between the loan companies are usually modeled as directed networks. Learning the informative low-dimensional representation of the networks is important for the default risk prediction of loan companies, even for the assessment of systematic financial risk level. In this paper, we propose a high-order graph attention representation method (HGAR) to learn the embedding of guarantee networks. Because this financial network is different from other complex networks, such as social, language, or citation networks, we set the binary roles of vertices and define high-order adjacent measures based on financial domain characteristics. We design objective functions in addition to a graph attention layer to capture the importance of nodes. We implement a productive learning strategy and prove that the complexity is near-linear with the number of edges, which could scale to large datasets. Extensive experiments demonstrate the superiority of our model over state-of-the-art method. We also evaluate the model in a real-world loan risk control system, and the results validate the effectiveness of our proposed approaches.","doi":"10.24963/ijcai.2019/807","url":"https://ijcai.org/proceedings/2019/807","pdf":"https://ijcai.org/proceedings/2019/0807.pdf","markdown":"https://ijcai.org/proceedings/2019/807.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/807","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"AI for Improving Human Well-being","track":"AI for Improving Human Well-being","number":807,"pages":{"first":5822,"last":5828},"keywords":["Special Track on AI for Improving Human-Well Being: AI benefits to society AI applications (Special Track on AI and Human Wellbeing)","Special Track on AI for Improving Human-Well Being: Societal applications (Special Track on AI and Human Wellbeing)"],"videos":[],"references":[{"position":1,"raw":"[Ahmed et al., 2013] Amr Ahmed, Nino Shervashidze, Shravan Narayanamurthy, Vanja Josifovski, and Alexan- der J Smola. 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