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Introduction
While it is acknowledged that macro variables such as deregulation, lack of information among the bank customers, homogeneity of the banking business and government and political interferences are some of the causes of bank failures; internal (micro)-related factors such as reckless lending, corruption, fraud, stiff competition and management deficiencies have also contributed to bank failures ([6], [7] Chijoriga, 1997, 2000; [4] Basel, 2004; [21] Liou and Smith, 2006). Past and recent literature show that the majority of failures are due to poor risk management and non-use of prudential classification and risk assessment methods ([33] Williams, 1995). Recent financial crises have also proved the importance of proper credit risk assessment and the need to correctly predict financial failure.
Statistical methods such as discriminant and logit analyses approaches have been used to predict business failure or success ([14] Gepp and Kumar, 2008; [17] Karbhari and Sori, 2008). While many past studies have used multiple discriminant analysis (MDA) for credit scoring, few have used the MDA as a risk assessment model. One of the problems in trying to get a risk assessment model has been about which variables to include in a credit scoring model (CSM). The purpose of this research was to investigate whether the inclusion of risk assessment variables in the MDA model improved a bank's ability to make correct customer classifications and credit risk assessments thereby improving management decisions. Specifically, the study intended to assess what variables are important in order to correctly classify, discriminate, predict, and assess the credit risk of a customer or an applicant and whether an MDA as a credit scoring method can improve management decision making.
Credit scoring methods and application of MDA
A credit scoring method is an empirically derived and statistically sound valuation device for estimating the likelihood that a customer will not pay his obligations when due. Credit scoring methods use data on observed borrowers' characteristics either to calculate the probability of default or to sort borrowers into different default classes. Credit scoring methods are both quantitative and qualitative in nature. The quantitative methods use statistical or mathematical methods to classify firms between groups, while the qualitative ones are more judgmental and subjective in nature. The major disadvantage of qualitative methods is that there is no...