{"type":"ScholarlyArticle","title":"Risk Guarantee Prediction in Networked-Loans","authors":[{"name":"Dawei Cheng","slug":"dawei-cheng","given":"Dawei","family":"Cheng","affiliation":"MoE Key Lab of Artificial Intelligence , Department of Computer Science and Engineering , Shanghai Jiao Tong University , China"},{"name":"Xiaoyang Wang","slug":"xiaoyang-wang","given":"Xiaoyang","family":"Wang","affiliation":"Zhejiang Gongshang University , China"},{"name":"Ying Zhang","slug":"ying-zhang","given":"Ying","family":"Zhang","affiliation":"University of Technology Sydney , Australia"},{"name":"Liqing Zhang","slug":"liqing-zhang","given":"Liqing","family":"Zhang","affiliation":"MoE Key Lab of Artificial Intelligence , Department of Computer Science and Engineering , Shanghai Jiao Tong University , China"}],"abstract":"The guaranteed loan is a debt obligation promise that if one corporation gets trapped in risks, its guarantors will back the loan. When more and more companies involve, they subsequently form complex networks. Detecting and predicting risk guarantee in these networked-loans is important for the loan issuer. Therefore, in this paper, we propose a dynamic graph-based attention neural network for risk guarantee relationship prediction (DGANN). In particular, each guarantee is represented as an edge in dynamic loan networks, while companies are denoted as nodes. We present an attention-based graph neural network to encode the edges that preserve the financial status as well as network structures. The experimental result shows that DGANN could significantly improve the risk prediction accuracy in both the precision and recall compared with state-of-the-art baselines. We also conduct empirical studies to uncover the risk guarantee patterns from the learned attentional network features. The result provides an alternative way for loan risk management, which may inspire more work in the future.","doi":"10.24963/ijcai.2020/618","url":"https://ijcai.org/proceedings/2020/618","pdf":"https://ijcai.org/proceedings/2020/0618.pdf","markdown":"https://ijcai.org/proceedings/2020/618.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/618","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":618,"pages":{"first":4483,"last":4489},"keywords":["AI for risk and security: AI for financial network risk","Foundation for AI in FinTech: Data mining and knowledge discovery for FinTech","AI for banking: AI for credit loan"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24113/file_24113.m3u8","length_sec":281,"provider":"hls"}],"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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