Due to advanced technology that may be associated with large and unorganized data, credit risk has been difficult to determine in our current world. We have therefore, fitted a logistic model using logistic regression in the data to help us determine how bank defaulting can be reduced. We found out that logistic regression model remains one of the most widely used classification technique in credit risk modeling and it is important that banks and other financial institutions use this model among others to credit bank acceptance strategies for every for every loan application thereby minimizing the bad loan error rate from their portfolio.