{"type":"ScholarlyArticle","title":"RisQNet: Rescuing SMEs from Financial Shocks with a Novel Networked-Loan Risk Assessment","authors":[{"name":"Zhaoyuan Lu","slug":"zhaoyuan-lu","given":"Zhaoyuan","family":"Lu","affiliation":"School of New Media and Communication , Tianjin University"},{"name":"Taijun Li","slug":"taijun-li","given":"Taijun","family":"Li","affiliation":"College of Intelligence and Computing , Tianjin University"},{"name":"Jingzhen Zhang","slug":"jingzhen-zhang","given":"Jingzhen","family":"Zhang","affiliation":"College of Intelligence and Computing , Tianjin University"},{"name":"Moyang Liu","slug":"moyang-liu","given":"Moyang","family":"Liu","affiliation":"College of Intelligence and Computing , Tianjin University"},{"name":"Xiang Li","slug":"xiang-li","given":"Xiang","family":"Li","affiliation":"China Iron and Steel Research Institute Group; University of California , Berkeley"},{"name":"Linyi Cui","slug":"linyi-cui","given":"Linyi","family":"Cui","affiliation":"Bank of China"},{"name":"Junqi Chen","slug":"junqi-chen","given":"Junqi","family":"Chen","affiliation":"College of Intelligence and Computing , Tianjin University; The State Council Leading Group Office of Information Center"},{"name":"Zhibin Niu","slug":"zhibin-niu","given":"Zhibin","family":"Niu","affiliation":"School of New Media and Communication , Tianjin University; College of Intelligence and Computing , Tianjin University"}],"abstract":"In the face of economic downturns, Small and Medium-sized Enterprises (SMEs) within interconnected networked-loans are vulnerable to cascading debt crises, exacerbated by factors like social media-induced financial shocks. Traditional risk assessment models, which mainly rely on financial data, inadequately predict such crises, as evidenced by the collapse of Silicon Valley Bank in 2023. To address this issue, we developed RisQNet, a model that uses temporal graph networks to incorporate diverse risks, including real-time media influences. This approach not only advances risk prediction through news feature extraction and large language models but also enhances risk management strategies with intuitive visualization tools. Validated on a dataset with a total loan volume of USD 3 trillion, RisQNet outperforms the state-of-the-art baseline and achieves 87.1% of AUC. Our collaborative effort with financial regulators and the SME community underpins the model's development, aligning with the UN SDG 8. RisQNet represents a significant step forward in leveraging AI for financial stability, offering a promising approach to combat the propagation of debt crises in financial networks.","doi":"10.24963/ijcai.2024/817","url":"https://ijcai.org/proceedings/2024/817","pdf":"https://ijcai.org/proceedings/2024/0817.pdf","markdown":"https://ijcai.org/proceedings/2024/817.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/817","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":817,"pages":{"first":7385,"last":7393},"keywords":["Multidisciplinary Topics and Applications: General","Data Mining: General"],"videos":[],"references":[{"position":1,"raw":"[Achakzai and Juan, 2022] Muhammad Atif Khan Achakzai and Peng Juan. 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