This article analyzes credit risk in the financial sector and proposes a methodology to improve its prediction accuracy using boosting algorithms such as XGBoost, LightGBM, and Boosted Random Forest. Datasets from the UCI Machine Learning Repository were used, including Statlog German Credit Data, Australian Credit Approval, and Bank Marketing. The methodology involved feature engineering, exploratory data analysis, and hyperparameter tuning. Additionally, a complementary strategy using K-means clustering was implemented to enhance the data. The results show that XGBoost outperforms the other models in various scenarios, and boosting-based methods deliver better performance than traditional approaches like decision trees and factorization machines—offering valuable insights for financial institutions.