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Cbkp_redit Default Risk Analysis Using Machine Learning Algorithms with Hyperparameter Optimization

Abstract:

Machine learning models are an important tool that provide a scientific method to identify potential debtors early and pbkp_redict which clients are more likely to default on their debts, improving the accuracy of assessment in cbkp_redit risk analysis in financial companies. The purpose of this study was to analyze the performance of gradient boosting machine learning algorithms (CatBoost, LightGBM, and XGBoost) in pbkp_redicting customer default risk, and the ability of the RandomUnderSampler sampling technique to address unbalanced categories of cbkp_redit risk. The exploratory analysis of the data set was carried out, then the data preprocessing, finally the training with hyperparameter adjustments with the GridSearchCV method to identify the largest number of clients with cbkp_redit risk. The model is evaluated based on metrics of sensitivity, specificity and precision, on a set of consumer cbkp_redit data. Among the proposed algorithms, XGBoost outperformed the LightGBM and catBoost models. Experimental results confirmed that the XGBoost model performs better for cbkp_redit risk pbkp_rediction with historical data.