The Business of advancing credits is gradually becoming a major target for many banks, as a result there is high competition among the financial institutions leading to default of most credits. In order to raise the quality of advancing credits and reducing the risk involved thereafter, CSM’s have been developed to improve the process of assessing credit worthiness during the credit evaluation process. Previous repayments, demographic characteristics and statistical techniques were used in constructing the LR model with P(D = 1) = 1 1+e−z to identify the important demographic characteristics related to credit risk. The results showed that DR is higher in males than in females. Married customers defaulted more than the singles and the higher the number of dependents, the higher the DR. The self-employed clients defaulted more than salary earners. Also, the higher the amount of loan collected, the higher the PD. With the knowledge of LR, it is possible to determine the credit worthiness of a borrower which may decrease bad debts, and help to set risk based credit pricing for the clients and make the credit advancing faster and more accurate.