In banking, loan approvals involve risk, managed by credit scores based on factors like income, credit history, and debt ratio. Traditional methods, using fixed rules, lack flexibility and fail to capture the complexity of changing client profiles.
XGBoost (eXtreme Gradient Boosting) is a powerful machine learning algorithm well-suited for credit prediction due to several key factors:
High Performance XGBoost combines weak decision trees into a robust model, improving prediction accuracy, crucial in credit where precise decisions have significant financial consequences.
Handling Imbalanced Data It adjusts class weights, improving performance on minority classes, important in credit approval scenarios where approvals are fewer than rejections.
Integrated Regularization XGBoost uses L1 and L2 regularization, preventing overfitting and improving the model's ability to generalize, reducing errors in new client evaluations.
Efficiency and Speed Optimized for speed, XGBoost handles large datasets quickly, essential in fast-paced business environments with high data volumes.