The collective revelation of credit institutions as regards the imminence of specific risks materialising, which often follows long periods of underestimating probable losses, can trigger a broad-based financial delev...The collective revelation of credit institutions as regards the imminence of specific risks materialising, which often follows long periods of underestimating probable losses, can trigger a broad-based financial deleveraging via an overly high upsurge in banks＇ risk premiums vis-a-vis the dynamics of fundamentals underlying loan repayment capability. In this context, this paper seeks to investigate the banking sector＇s internal mechanisms that might bring about a negative spiral of credit risk by building a model for the interaction between the increase of the risk premium and that of net interest income and provisioning rate. Statistical results confirm that a higher risk premium is one of the major determinants of credit default in Romania and its excessive widening could affect financial stability in Romania.展开更多
In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification...In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification accuracy. To evaluate the prediction accuracy of the model, we compare its performance with those of SVM, linear discriminate analysis, logistic regression analysis, K-nearest neighbors, classification and regression tree, neural network and PCA-NN. The experimental results show the model have a very good prediction accuracy展开更多
文摘The collective revelation of credit institutions as regards the imminence of specific risks materialising, which often follows long periods of underestimating probable losses, can trigger a broad-based financial deleveraging via an overly high upsurge in banks＇ risk premiums vis-a-vis the dynamics of fundamentals underlying loan repayment capability. In this context, this paper seeks to investigate the banking sector＇s internal mechanisms that might bring about a negative spiral of credit risk by building a model for the interaction between the increase of the risk premium and that of net interest income and provisioning rate. Statistical results confirm that a higher risk premium is one of the major determinants of credit default in Romania and its excessive widening could affect financial stability in Romania.
文摘In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification accuracy. To evaluate the prediction accuracy of the model, we compare its performance with those of SVM, linear discriminate analysis, logistic regression analysis, K-nearest neighbors, classification and regression tree, neural network and PCA-NN. The experimental results show the model have a very good prediction accuracy
