As data is now becoming available and accessible, credit risk managementdepartments in financial institutions are now engaging machine learning techniques to produce more reliable internal credit risk rating systems. In this paper, data on 17Zimbabwean banks are used to apply and test the Logical Analysis of Data (LAD) - asupervised learning data mining technique, to generate an objective, transparent,consistent, accurate, self- contained and generalisable credit risk rating system that hasvarying levels of granularity and is Basel compliant. This system gives an understandingof relationships between the uses of credit ratings, the different options for ratingsystem design and the effectiveness of internal credit rating systems. Such a systembecomes useful in decision making pertaining to the determination of the amountallocated as regulatory capital in banks, which is a buffer in banks against distress andbank failure

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