Article ID: | iaor201525201 |
Volume: | 9 |
Issue: | 4 |
Start Page Number: | 240 |
End Page Number: | 245 |
Publication Date: | Dec 2014 |
Journal: | International Journal of Simulation and Process Modelling |
Authors: | Hong Bo, Xie Xingsheng, Guo Haoming |
Keywords: | time series: forecasting methods |
The probability of default (PD) is an important parameter to quantify credit risk, which is usually impacted by some less frequent accidents, such as market factors or the macroeconomic climate changes. The traditional approaches to estimate PD, such as expert‐method or pattern classification based on a set of financial indicators, are heavily dependent on financial reports offered by borrow‐customers, and can lead to unreliability and long‐time lags in forecast. According to the business schema of loan‐fund expenditure surveillance in some commercial banks in China, this paper proposes a support vector machine (SVM) PD classifier constructed on a set of loan‐fund expenditure behaviour features, which can be directly collected from the fund trading databases of the banks in time. For the sake of comparison, both of the Logistic and SVM models are tested in this paper to predict PD, and their classification accuracy can be up to 84.6% and 89.4% respectively.