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 nodesin 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 systemto demonstrate the effectiveness of proposed model .
Uncertain graphs have been widely used to model complex linked data in many real-world applications, such as guaranteed-loan networks and power grids, where a node or edge may be associated with a probability. In these networks, a node usually has a certain chance of default or breakdown due to self-factors or the influence from upstream nodes. For regulatory authorities and companies, it is critical to efficiently identify the vulnerable nodes, i.e., nodes with high default risks, such that they could pay more attention to these nodes for the purpose of risk management. In this paper, we propose and investigate the problem of top-k vulnerable nodes detection in uncertain graphs. We formally define the problem and prove its hardness. To identify the k most vulnerable nodes, a sampling-based approach is proposed. Rigorous theoretical analysis is conducted to bound the quality of returned results . Novel optimization techniques and a bottom-k sketch based approach are further developed in order to scale for large networks. In the experiments, we demonstrate the performance of proposed techniques on 3 real financial networks and 5 benchmark networks. The evaluation results show that the proposed methods can achieve up to 2 orders of magnitudes speedup compared with the baseline approach. Moreover, to further verify the advantages of our model in real-life scenarios, we integrate the proposed techniques with our current loan risk control system, which is deployed in the collaborated bank, for more evaluation. Particularly, we show that our proposed new model has superior performance on real-life guaranteed-loan network data, which can better predict the default risks of enterprises compared to the state-of-the-art techniques .
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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
Uncertain graphs have been widely used to model complex linked data in many
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such as guaranteed-loan networks and power grids, where a node or edge may be associated with a probability. In these networks, a node usually has a certain chance of default or breakdown due to self-factors or the influence from upstream nodes. For regulatory authorities and companies,
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detect top vulnerable nodesin such complex financial network with near real-time performance. To address this challenge, we introduce VulnDS: a
efficiently identify the vulnerable nodes, i.e., nodes with high default risks, such that they could pay more attention to these nodes for the purpose of risk management. In this paper, we propose and investigate the problem of
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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
nodes detection in uncertain graphs. We formally define the problem and prove its hardness. To identify the k most vulnerable nodes,
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with tight theoretical guarantee
is proposed. Rigorous theoretical analysis is conducted to bound the quality of returned results
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and a bottom-k sketch based approach are further
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100x speedup ratio compared with baseline methods. Case studies are further conducted in the deployed systemto demonstrate the effectiveness of proposed model
2 orders of magnitudes speedup compared with the baseline approach. Moreover, to further verify the advantages of our model in real-life scenarios, we integrate the proposed techniques with our current loan risk control system, which is deployed in the collaborated bank, for more evaluation. Particularly, we show that our proposed new model has superior performance on real-life guaranteed-loan network data, which can better predict the default risks of enterprises compared to the state-of-the-art techniques