Groups of small and medium enterprises (SMEs) can back each other to obtain loans from banks and thus form guarantee networks. If the loan repayment of a small business in the network defaults, its backers are required to repay the loan. Therefore, risk over networked enterprises may cause significant contagious damage. In real-world applications, it is critical to detect top vulnerable nodes in such complex financial network with near real-time performance. To address this challenge, we introduce VulnDS: a top-k vulnerable SME detection system for large-scale financial networks, which is deployed in our collaborated bank. First, we model the risks of the guaranteed-loan network by a probabilistic graph, which consists of the guarantee-loan network structure, self-risks for the nodes , and diffusion probability for the edges. Then, we propose a sampling-based approach with a tight theoretical guarantee. To scale for large networks, novel optimization techniques are developed  . We conduct extensive experiments on three financial datasets, in addition with 5 large-scale benchmark networks. The evaluation results show that the proposed method can achieve over 10-100x speedup ratio compared with baseline methods  .
Groups of small and medium enterprises (SMEs) can back each other to obtain loans from banks and thus form guarantee networks. If the loan repayment of a small company in the network defaults, its backers are required to repay the loan. Therefore, risk over networked enterprises may cause significant contagious damage. In real-world applications, it is critical to detect top vulnerable nodes in such complex financial network with near real-time performance. To address this challenge, we introduce VulnDS: a top-k vulnerable SME detection system for large-scale financial networks, which is deployed in our collaborated bank. First, we model the risks of the guaranteed-loan network by a probabilistic graph, which consists of the guarantee-loan network structure, self-risk probability for the nodes and diffusion probability for the edges. Moreover, to identify the vulnerable enterprises, we propose a sampling-based approach with tight theoretical guarantee. Novel optimization techniques are developed in order to scale for large networks . We conduct extensive experiments on 3 real financial datasets, in addition with 5 large-scale benchmark networks. The evaluation results show that the proposed method can achieve up to 100x speedup ratio compared with baseline methods . Case studies are further conducted in the deployed system to demonstrate the effectiveness of proposed model .
R
business
company
R
self-risks
self-risk probability
D
,
R
Then,
Moreover, to identify the vulnerable enterprises,
D
a
R
To scale for large networks, novel
Novel
A
in order to scale for large networks
R
three
3 real
R
over 10-100x
up to 100x
A
. Case studies are further conducted in the deployed system to demonstrate the effectiveness of proposed model