Comparative Analysis of Statistical models in Credit Assessment

Abstract

With the emergence of the current financial crisis, important advances have been made in credit risk management. Inherent in this management process is the assessment of creditworthiness routine which subsequently leads to a credit granting decision. This study is aimed at developing a statistical model that can be used to ascertain credit assessment and to predict the probability of default of firms seeking credit from a Ghanaian commercial bank. Subsequently, an attempt was made to find financial ratios that can best be made used to successfully construct the model. To achieve these purposes, the study employed the Probit and logit models for comparative reasons in terms of their predictive abilities. Performance of the models was assessed using the percentage correctly classified (PCC) and the area under the receiver operating characteristics curved (AUC) where significant differences between the two models were observed. It was found that both the Probit and the logit classifiers yield very good performance rates but the logit model performed better for credit scoring. It was also found that ratios bordering on assets to liability ratios, account receivable to liability, Cash to Assets, current liability to total liabilities , Net current asset ,and total asset firm size are those that were significantly helpful in scoring credit applicant. Practically the model assist in reducing the time spent on evaluating credit applicant of each firm subject to the model and also serve as a difference between application serving and portfolio management . Indeed the multiplier effect will be a significant improvement in loan portfolio quality of the model user.