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iConViz: Interactive Visual Exploration of the Default Contagion Risk of Networked-Guarantee Loans

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Listed:
  • Zhibin Niu
  • Runlin Li
  • Junqi Wu
  • Dawei Cheng
  • Jiawan Zhang

Abstract

Groups of enterprises can serve as guarantees for one another and form complex networks when obtaining loans from commercial banks. During economic slowdowns, corporate default may spread like a virus and lead to large-scale defaults or even systemic financial crises. To help financial regulatory authorities and banks manage the risk associated with networked loans, we identified the default contagion risk, a pivotal issue in developing preventive measures, and established iConVis, an interactive visual analysis tool that facilitates the closed-loop analysis process. A novel financial metric, the contagion effect, was formulated to quantify the infectious consequences of guarantee chains in this type of network. Based on this metric, we designed and implement a series of novel and coordinated views that address the analysis of financial problems. Experts evaluated the system using real-world financial data. The proposed approach grants practitioners the ability to avoid previous ad hoc analysis methodologies and extend coverage of the conventional Capital Accord to the banking industry.

Suggested Citation

  • Zhibin Niu & Runlin Li & Junqi Wu & Dawei Cheng & Jiawan Zhang, 2020. "iConViz: Interactive Visual Exploration of the Default Contagion Risk of Networked-Guarantee Loans," Papers 2006.09542, arXiv.org, revised Aug 2020.
    • Handle: RePEc:arx:papers:2006.09542
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    References listed on IDEAS

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    Cited by:

    1. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    2. Zhibin Niu & Junqi Wu & Dawei Cheng & Jiawan Zhang, 2021. "Regshock: Interactive Visual Analytics of Systemic Risk in Financial Networks," Papers 2104.11863, arXiv.org.
    3. Niu, Zhibin & Wu, Junqi & Liu, Xiufeng & Huang, Lizhen & Nielsen, Per Sieverts, 2021. "Understanding energy demand behaviors through spatio-temporal smart meter data analysis," Energy, Elsevier, vol. 226(C).

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