  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.
