🏦 Credit Risk Loan Evaluation System Simulation
📝 Overview
This project is a functional, simplified simulation of a Credit Risk Loan Evaluation System. It models real-world bank policies to automatically assess loan applications, determine the application status (Approved, Rejected, or Conditional), and generate all necessary formal documentation in real-time.
It demonstrates a foundation for turning complex, written financial policies into an instant, automated digital decision-making process.
✨ Features
Policy Management: Three distinct loan policies (Policy-1, Policy-2, Policy-3) are defined, each with specific criteria (Min. Age, Min. Credit Score, Max. Loan Limit).
Automated Documentation (PDF): Automatically generates and saves formal PDF documents for:
The Loan Policies themselves (serving as the digital "Rulebook").
Individual Loan Applications (providing a complete record for audit).
Evaluation Engine: A frontend mechanism allows comparison of simulated applications against the selected policy, resulting in clear status classifications: Strongly Approved, Approved with Conditions, or Rejected.
User Interface: A minimal React front-end simulates the interaction, allowing policy selection and viewing of instant evaluation results.
🛠️ Technology Stack
This project is split into two primary environments:
Component
Technology
Role
PDF Generation / Backend Logic
Python (via fpdf2)
Automates the creation of policy and application PDF documents.
Frontend Simulation
React (JavaScript)
Provides the interactive user interface for evaluation.
Dependencies
Python (for PDF Generation)
The following packages are required, as listed in requirements.txt:
fpdf2==2.7.4
requests==2.31.0
React (for Frontend)
Standard React dependencies are managed via package.json and package-lock.json.
🚀 Getting Started
To set up and run this project locally, you will need Python (3.x) and Node.js / npm installed.
- Clone the Repository
git clone [YOUR_REPO_URL] cd [your-project-directory]
- Python Setup (PDF Generation)
Navigate to the Python environment folder (if applicable) or run the install command from the root if the script runs from there.
pip install -r requirements.txt
- React Setup (Frontend)
Navigate to the directory containing the React code and install dependencies.
npm install
npm start
The application should now be running in your browser, and you can proceed to generate policies and evaluate applications.
✅ Status
Status: Complete This prototype successfully demonstrates the core mechanics of policy-based automated loan evaluation and documentation.