Title: Visual analytics for loan guarantee network risk management,
Abstract: Groups of enterprises guarantee each other and form complex guarantee
networks when they try to obtain loans from banks. Such secured loan can
enhance the solvency and promote the rapid growth in the economic upturn
period. However, potential systemic risk may happen within the risk binding
community. Especially, during the economic down period, the crisis may spread
in the guarantee network like a domino. Monitoring the financial status,
preventing or reducing systematic risk when crisis happens is highly concerned
by the regulatory commission and banks. We propose visual analytics approach
for loan guarantee network risk management, and consolidate the five analysis
tasks with financial experts: i) visual analytics for enterprises default risk,
whereby a hybrid representation is devised to predict the default risk and
developed an interface to visualize key indicators; ii) visual analytics for
high default groups, whereby a community detection based interactive approach
is presented; iii) visual analytics for high defaults pattern, whereby a motif
detection based interactive approach is described, and we adopt a Shneiderman
Mantra strategy to reduce the computation complexity. iv) visual analytics for
evolving guarantee network, whereby animation is used to help understanding the
guarantee dynamic; v) visual analytics approach and interface for default
diffusion path. The temporal diffusion path analysis can be useful for the
government and bank to monitor the default spread status. It also provides
insight for taking precautionary measures to prevent and dissolve systemic
financial risk. We implement the system with case studies on a real-world
guarantee network. Two financial experts are consulted with endorsement on the
developed tool. To the best of our knowledge, this is the first visual
analytics tool to explore the guarantee network risks in a systematic manner.