This research under the domain cyber security investigates and compares the effectiveness of two leading gradient boosting algorithms—LightGBM and XGBoost—in predicting credit default, over the Home Credit dataset. The initial model using LightGBM, applied to application-level data, reached an AUC score of 0.75. After enriching the dataset by integrating external credit bureau records, its performance improved notably to an AUC of 0.90. Further tuning and training of XGBoost on the same enriched dataset, the model reached an AUC of 0.9998, accompanied by exceptional precision and recall. For greater interpretability, SHAP (SHapley Additive exPlanations) analysis provides a logical view for financial analysts and data scientists in achieving model efficiency, fairness, and interpretability in their decision-making. SHAP was used to explain which key features helps in model’s prediction results, with EXT_SOURCE_3, DAYS_BIRTH, and REGION_RATING_CLIENT as example features. The comparative study highlights the difference between accuracy, processing time, and transparency in model selection to provide valuable information in credit risk modeling for actual financial applications. The future scope includes implementing the best performed model’s prediction in real world.