Groups of Small and Medium Enterprises (SME) back each other and form guarantee network to obtain loan from banks . The risk over the networked enterprises may cause significant contagious damage. To dissolve such risks , we propose  a hybrid feature representation, which is feeded into a gradient boosting model for credit risk assessment of guarantee network. Empirical study is performed on a ten-year guarantee loan record from commercial banks. We find that often hundreds or thousands of enterprises back each other and constitute a sparse complex network. We study the risk of various structures of loan guarantee network, and observe the high correlation between defaults with centrality, and with the communities of the network. In particular , our quantitative risk evaluation model shows promising prediction performance on real-world data, which can be useful to both regulators and stakeholders.
Networked-guarantee loans may cause the systemic risk related concern of the government and banks in China. The prediction of default of enterprise loans is a typical extremely imbalanced prediction problem, and the networked-guarantee make this problem more difficult to solve. Since the guaranteed loan is a debt obligation promise, if one enterprise in the guarantee network falls into a financial crisis, the debt risk may spread like a virus across the guarantee network, even lead to a systemic financial crisis. In this paper , we propose an imbalanced network risk diffusion model to forecast the enterprise default risk in a short future. Positive weighted k-nearest neighbors (p-wkNN) algorithm is developed for the stand-alone case -- when there is no default contagious; then a data-driven default diffusion model is integrated to further improve the prediction accuracy. We perform the empirical study on a real-world three-years loan record from a major commercial bank. The results show that our proposed method outperforms conventional credit risk methods in terms of AUC. In summary , our quantitative risk evaluation model shows promising prediction performance on real-world data, which could be useful to both regulators and stakeholders.
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Groups of Small and Medium Enterprises (SME) back each other and form guarantee network to obtain loan from banks . The risk over the networked enterprises may cause significant contagious damage. To dissolve such risks
Networked-guarantee loans may cause the systemic risk related concern of the government and banks in China. The prediction of default of enterprise loans is a typical extremely imbalanced prediction problem, and the networked-guarantee make this problem more difficult to solve. Since the guaranteed loan is a debt obligation promise, if one enterprise in the guarantee network falls into a financial crisis, the debt risk may spread like a virus across the guarantee network, even lead to a systemic financial crisis. In this paper
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an imbalanced network risk diffusion model to forecast the enterprise default risk in a short future. Positive weighted k-nearest neighbors (p-wkNN) algorithm is developed for the stand-alone case -- when there is no default contagious; then a data-driven default diffusion model is integrated to further improve the prediction accuracy. We perform the empirical study on
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hybrid feature representation, which is feeded into a gradient boosting model for credit risk assessment of guarantee network. Empirical study is performed on a ten-year guarantee
real-world three-years
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commercial banks. We find that often hundreds or thousands of enterprises back each other and constitute a sparse complex network. We study the risk of various structures of loan guarantee network, and observe the high correlation between defaults with centrality, and with the communities of the network. In particular
a major commercial bank. The results show that our proposed method outperforms conventional credit risk methods in terms of AUC. In summary
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