I will build a credit risk scoring model using python and machine learning

Need to predict credit risk or customer default probability? I build machine learning models that assess risk with both accuracy and business interpretability not just a black box.

What you'll get:

• Exploratory data analysis and feature preparation
• Predictive model (Logistic Regression or XGBoost, depending on scope)
• SHAP explainability, so you understand why the model predicts what it predicts
• Optional: probability calibration and Expected Credit Loss (ECL) framing aligned with industry regulatory standards (OJK/IFRS 9)

I've built end-to-end credit scoring pipelines achieving AUC 0.77+ and KS Statistic in the "Excellent" range with a strong focus on making results usable for real business decisions, not just academic metrics.

Tools I use: Python (XGBoost, scikit-learn, SHAP), Pandas

Not sure which package fits your data and goals? Message me before ordering and I'll help scope it correctly.