The objective of this study is to examine the performance of two default prediction models: the Z-score modelusing discriminant analysis, and the logit model on a dataset of 60 defaulted and 60 solvent companies. Financialratios obtained from corporate balance sheets are used as independent variables while solvent/defaulted company(ratings assigned) is the dependent variable. Furthermore, for logistic regressions, an attempt is made to combinemacro variables and dummy industry variables along with accounting ratios. The predictive ability of theproposed Z score model is higher when compared to both the Altman original Z-score model and the Altmanmodel for emerging markets. The research findings establish the superiority of logit model over discriminantanalysis and demonstrate the significance of accounting ratios in predicting default.