A Machine Learning Framework for Real-Time Credit Risk Assessment and Scoring

The rapid growth of digital financial services has heightened the demand for accurate,efficient, and interpretable credit risk assessment models. Conventional credit scoring methods often struggle with the complexity and volume of modern financial data. This research presents a comprehensive end-to-end framework for credit risk modeling, from data preprocessing to deployment as a real-time web application. The system utilizes a Gradient
Boosting (XGBoost) model trained on an extensive dataset including customer demographics, loan details, and credit bureau history. The model achieves exceptional discriminative performance, with an AUC of 98% and a Gini coefficient of 96%, surpassing industry standards. To address interpretability challenges, SHAP (SHapley Additive exPlanations) is integrated for localized model explanation. The model is deployed through a user-friendly Streamlit web interface, enabling loan officers to efficiently assess creditworthiness. This framework demonstrates a practical, predictive, and transparent solution for modern credit risk
management.