Comparative Analysis of Statistical models in Credit Assessment
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Date
2013-06
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Publisher
University of Ghana
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.
Description
Thesis (MPHIL) - University of Ghana, 2013