Defaults assessment of mortgage loan with rough set and SVM

Credit risk is the primary source of risk to financial institutions. Support vector machine (SVM) is a good classifier to solve binary classification problem. The learning results of SVM possess stronger robustness. We adjust these penalty parameters to achieve better generalization performances with using grid-search method in our application. In this paper the attribute reduction of rough set has been applied as preprocessor so that we can delete redundant attributes, then default prediction model of the housing mortgage loan is established by using SVM Classification performance is better than some other classification algorithms.