In this study, we propose a model that achieves both accurate modeling and sustainable model stability for corporate bankruptcy prediction. This model is to model the given samples accurately as well as to respond adequately to the unknown inputs by employing semiparametric approach where parametric model and nonparametric Neural Networks (NNs) are combined. By exploring the structural relationships within the available sample data, the proposed model is assumed to retain the advantages of both parametric and nonparametric models. The proposed model is compared to pure parametric models such as Multivariate Discriminant Analysis (MDA) and Logistic Regression (LR), and pure nonparametric model such as NNs. Each model predicts the default probability of a company and classifies the company into an appropriate group as either bankrupt or healthy. Experimental results demonstrated that the proposed semiparametric model showed superior performance in terms of model stability and prediction accuracy in bankruptcy prediction.