Abstract
Loan is becoming essential need in this modern life. Banks need to keep their NPL ratio low in order to maintain their financial health. One of customer’s screening techniques is credit scoring. Decision tree is a simple method to classify a condition into two different classes using given classifier, and widely used to perform credit scoring in the financial industry. We integrated Iterative Dichotomizer 3 and Boosted Decision Tree methods and used Microsoft Azure Machine Learning tools to perform credit score profiling. This study is cross sectional in time and using 600 instances data of loan submission in Tangerang, Indonesia. The result shows good performance with performance evaluation metric of accuracy, precision, recall, and F1 score are 0.85, 0.885, 0.793 and 0.836 respectively.
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