🔍 CreditLens: Enterprise Credit Risk Assessment & Model Validation Pipeline

An end-to-end automated credit risk scoring and validation framework designed for Model Risk Management (MRM) and Operational Risk Oversight.

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🚀 Overview

CreditLens is a lightweight, high-performance credit default risk assessment engine. Built specifically to handle portfolio stress-testing and Probability of Default (PD) calculations, it provides real-time model interpretability, statistical model validation, and an interactive executive intelligence portal.

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🛠️ Key Features

• Automated Risk Scoring Engine: Predicts live borrower default probabilities based on credit limits, repayment histories, and statement balances.
• Model Validation Metrics: Computes out-of-sample performance metrics including ROC-AUC scores and detailed classification reports.
• Interactive MI Dashboard: Built via Streamlit to enable real-time risk parameter stress-testing.
• Lightweight Architecture: Optimized for low-footprint execution.

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📂 Project Structure

CreditLens/
│
├── data/
│   └── credit_data.csv        # Primary credit default client dataset
├── models/
│   └── trained_model.pkl      # Serialized machine learning model artifact
├── app.py                     # Streamlit web application interface
├── train.py                   # Model training and validation script
└── requirements.txt           # Project dependencies