The new evolving technologies bring new insights of supporting decision making in different areas like finance, marketing, production, social world, healthcare and are showing positive results. Devices and sensors become smart and are helping in a variety of areas like predicting failure and preventive maintenance. The main risk for many organizations comes from the low level of risk prediction. Banks and Micro Finance Institutions are highly challenged by the low level of credit default risk estimation. Loan status conditions in banks and microfinance institutions fall in the three classes of normal loan, substandard loan and loss loan depending on the loan repayment of the customer. This research aimed to develop a credit default prediction model by using loan and customer information with data mining methods. To build a model a six step cross industry standard for data mining process model was applied. For model building, python data miner was used. For this research we used 73,596 loan data from Amhara credit and saving institution. For model building random forest, k-nearest neighbor and decision tree algorithms were tested and evaluated. Random forest algorithm achieved the best accuracy of 96.81%. The results of these algorithms were tested with accuracy measurement methods of confusion matrix.