Credit Risk Prediction Model Using Feature Engineering and Machine Learning Techniques
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Abstract
Credit scoring is a crucial step in the risk management process of the financial industry and commercial banks. The objective of this research is the development of a credit risk prediction model using feature engineering and machine learning techniques. This research was used to test the algorithm with a peer-to-peer (P2P) lending dataset and measure performance with classification accuracy. The experiment in this research found the XGB algorithm provided the most effective classification accuracy of 88.94%, which is better than other classifiers. Therefore, the proposed
research framework of this research, working with feature engineering, feature selection, and machine learning techniques, is suitable and effective for credit scoring problem analysis.
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