<![CDATA[Credit scoring has a very important role in the financial industry to assess the eligibility of loan applicants and mitigate credit risk. However, the main challenge in credit scoring modeling is the large number of features that need to be considered. Feature selection becomes an inevitable step to improve model performance. This research proposes the use of hybrid ensemble boosting techniques through XGBoost, LightGBM, and CatBoost methods, as well as aggregation techniques for feature selection, the results of which are then used to build predictive models using Random Forest. Experimental results show that the aggregation technique using feature slices selected by the three methods provides the best model with the least number of features, which is only about 11% of the total features. The use of fewer features not only increases the computational speed and efficiency of the model but also improves the generalization ability, which allows the model to perform better on new data. In addition, this model shows the smallest difference between train accuracy and mean cross-validation score, indicating high model stability and reliability.]]>
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