The main purpose of credit risk assessment is to help financial institutions identify applicants with good credit and eliminate applicants with bad credit, minimizing the risk of capital loss and maximizing returns. Recent years have witnessed excellent performance of machine learning in the credit risk prediction. This paper extends the previous research by applying two boosting algorithms, namely AdaBoost and XGboost, to perform the credit scoring for real data from Lending Club. Compared with two statistical methods and three individual classifiers, the results show that (i) AdaBoost and XGBoost obtain higher forecasting accuracy for credit risk, providing stronger discrimination ability. (ii) AdaBoost has a greater ability to discriminate minority classes (defaulters), which can reduce capital losses for institutions. (iii) XGBoost is able to capture more potential benefits for institutions because it is more accurate in identifying majority classes, i.e., non-defaulters.
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