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📋 Executive Summary

This project simulates a Real-World Credit Risk Monitoring System.

Leveraging my 15+ years of experience in Banking and Risk Management, I engineered an automated pipeline to replace traditional manual reporting. This solution ingests raw credit data, processes it through a Dockerized PostgreSQL database, applies business logic via SQL Views, and visualizes critical KPIs (Vintage Analysis, Default Rates) in a Streamlit web application.

Key Features:

• End-to-End Pipeline: From raw data (ETL) to actionable insights.
• Infrastructure as Code: Fully containerized database using Docker.
• Business Logic in SQL: robust risk segmentation (Age vs. Product) calculated directly in the DB layer.

🛠️ Tech Stack

• Language: Python 3.10+
• Database: PostgreSQL 15 (Docker Container)
• ETL & Analysis: Pandas, SQLAlchemy
• Visualization: Streamlit, Plotly
• Version Control: Git

🚀 How to Run locally

1. Clone the repo:

   git clone https://github.com/Donavid/credit-risk-dashboard.git
   cd credit-risk-dashboard

2. Start the Infrastructure:

   docker-compose up -d

3. Install Dependencies:

   python -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

4. Load Data (ETL):

   python etl.py

5. Launch Dashboard:

   streamlit run dashboard.py

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Developed by Donar Vidal - Financial Data Analyst