VulnDS: Top-k Vulnerable SME Detection System in Networked-Loans

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.