Explainable Boosted Scoring with Python: turning XGBoost, LightGBM, and CatBoost into explainable scorecards

An AI-driven credit risk management platform using alternative data, psychometrics, and explainable ML to expand financial inclusion.

This is a machine learning project for credit decisioning for banks or other financial institutions and in this project, we will use machine learning models for classification.

Built and deployed a Flask-based machine learning system to predict loan default risk using customer demographics and financial indicators. Applied advanced ensemble models like XGBoost and LightGBM to achieve ~99% accuracy. Designed a full-stack solution with real-time prediction capabilities, enabling faster, smarter loan decisions in banking.

A SAS-driven analysis using logistic regression and statistical modeling to predict loan default risks, providing lenders with actionable insights for risk mitigation and portfolio management.