CreditTheory Overall Proposal- Automated Credit Analysis Platform

creditTheory is a comprehensive credit analysis and monitoring platform designed for banking institutions and financial analysts. It automates credit scoring, financial statement analysis, and covenant monitoring to streamline the credit review process.

Table of Contents

• Features
• Technology Stack
• Prerequisites
• Installation
• Running the Application
• API Documentation
• Project Structure
• Database Schema
• Credit Scoring Methodology
• Troubleshooting

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Features

Core Functionality

• Automated Credit Scoring: 0-100 scale credit scoring with letter ratings (AAA to B-)
• Financial Statement Analysis: Comprehensive analysis of balance sheets, income statements, and cash flow
• Covenant Monitoring: Real-time tracking of loan covenants with breach detection
• Risk Assessment: Multi-dimensional risk evaluation across liquidity, leverage, profitability, and coverage
• Portfolio Management: Monitor multiple companies simultaneously

Key Metrics Calculated

• Liquidity Ratios: Current Ratio
• Leverage Ratios: Debt/Equity, Debt/EBITDA
• Coverage Ratios: Interest Coverage
• Profitability Ratios: Profit Margin, EBITDA Margin, ROE

Covenant Types Supported

1. Debt/EBITDA
2. Debt/Equity
3. Current Ratio
4. Interest Coverage
5. Fixed Charge Coverage

---

Technology Stack

Backend

• Python 3.8+: Core programming language
• Flask: Lightweight web framework
• SQLAlchemy: ORM for database operations
• SQLite: Database (easily replaceable with PostgreSQL/MySQL)
• Flask-CORS: Cross-origin resource sharing

Frontend

• React 18: UI library
• Recharts: Data visualization
• Lucide React: Icon library
• Tailwind CSS: Styling (utility classes)

---

Prerequisites

Before you begin, ensure you have the following installed:

1. Python 3.8 or higher

   python --version

2. Node.js 16+ and npm (for frontend development)

   node --version
   npm --version

3. pip (Python package manager)

   pip --version

---

Installation

Step 1: Clone or Download the Project

Create a project directory and add the files:

mkdir credittheory
cd credittheory

Place the following files in this directory:

• app.py (backend)
• requirements.txt
• README.md

Step 2: Set Up Python Virtual Environment

On macOS/Linux:

python3 -m venv venv
source venv/bin/activate

On Windows:

python -m venv venv
venv\Scripts\activate

You should see (venv) in your terminal prompt.

Step 3: Install Python Dependencies

pip install -r requirements.txt

This will install:

• Flask (web framework)
• Flask-CORS (cross-origin support)
• Flask-SQLAlchemy (database ORM)
• SQLAlchemy (database toolkit)

Step 4: Initialize the Database

The database will be automatically initialized when you first run the application. It includes seed data with 3 sample companies and their financial statements.

Step 5: Set Up the Frontend

Navigate to the frontend directory:

cd frontend

Install frontend dependencies:

npm install

This will install React, Recharts, and other required packages.

---

Running the Application

Option 1: Automated Setup (Recommended)

On Windows:

setup.bat

On macOS/Linux:

chmod +x setup.sh
./setup.sh

Option 2: Manual Setup

Start the Backend Server

1. Activate virtual environment:

   # Windows
   venv\Scripts\activate
   
   # macOS/Linux
   source venv/bin/activate

2. Run the Flask app:

   python app.py

You should see output like:

============================================================
credittheory Backend Server Starting
============================================================
Server running on: http://localhost:5000
API documentation: http://localhost:5000/api/health
============================================================

The backend API is now running! You can test it by visiting:

• Health check: http://localhost:5000/api/health
• Get companies: http://localhost:5000/api/companies

Start the Frontend

In a new terminal window:

1. Navigate to frontend directory:

   cd frontend

2. Start the React development server:

   npm start

The frontend will automatically open at http://localhost:3000

Important: Make sure the backend is running on port 5000 before starting the frontend.

---

API Documentation

Base URL

http://localhost:5000/api

Endpoints

1. Health Check

GET /api/health

Response:

{
  "status": "healthy",
  "message": "credittheory API is running"
}

2. Get All Companies

GET /api/companies

Response:

[
  {
    "id": 1,
    "name": "TechCorp Industries",
    "industry": "Technology",
    "credit_score": 78.5,
    "credit_rating": "A-",
    "risk_level": "Low",
    "created_at": "2024-01-01T00:00:00",
    "updated_at": "2024-12-01T00:00:00"
  }
]

3. Get Company Details

GET /api/companies/{company_id}

Response:

{
  "id": 1,
  "name": "TechCorp Industries",
  "industry": "Technology",
  "credit_score": 78.5,
  "credit_rating": "A-",
  "risk_level": "Low",
  "latest_statement": {
    "id": 4,
    "statement_date": "2024-12-01",
    "period_type": "Q4",
    "ratios": {
      "current_ratio": 1.85,
      "debt_to_equity": 0.65,
      "debt_to_ebitda": 2.1,
      "interest_coverage": 8.5,
      "profit_margin": 18.5,
      "ebitda_margin": 28.3,
      "roe": 15.2
    }
  },
  "covenants": [
    {
      "id": 1,
      "covenant_type": "Debt/EBITDA",
      "threshold": 3.0,
      "current_value": 2.1,
      "status": "Compliant"
    }
  ]
}

4. Create Company

POST /api/companies
Content-Type: application/json

{
  "name": "New Company Inc",
  "industry": "Healthcare"
}

5. Add Financial Statement

POST /api/companies/{company_id}/financial-statements
Content-Type: application/json

{
  "statement_date": "2024-12-31",
  "period_type": "Annual",
  "total_assets": 10000000,
  "current_assets": 4000000,
  "total_liabilities": 6000000,
  "current_liabilities": 2000000,
  "total_debt": 3000000,
  "shareholders_equity": 4000000,
  "revenue": 15000000,
  "operating_expenses": 10000000,
  "ebitda": 5000000,
  "interest_expense": 200000,
  "net_income": 3000000
}

6. Get Financial Statements

GET /api/companies/{company_id}/financial-statements

7. Add Covenant

POST /api/companies/{company_id}/covenants
Content-Type: application/json

{
  "covenant_type": "Debt/EBITDA",
  "threshold": 3.0
}

8. Get Covenants

GET /api/companies/{company_id}/covenants

9. Dashboard Summary

GET /api/dashboard/summary

Response:

{
  "total_companies": 3,
  "risk_distribution": {
    "low": 1,
    "medium": 1,
    "high": 1
  },
  "covenant_status": {
    "total": 12,
    "breached": 1,
    "warning": 2,
    "compliant": 9
  }
}

---

📁 Project Structure

credittheory/
├── app.py                      # Main Flask application
├── requirements.txt            # Python dependencies
├── README.md                   # Documentation
├── setup.sh                    # Setup script (Mac/Linux)
├── setup.bat                   # Setup script (Windows)
├── credittheory.db             # SQLite database (auto-generated)
├── venv/                      # Virtual environment (auto-generated)
└── frontend/                  # React frontend
    ├── package.json           # Frontend dependencies
    ├── README.md              # Frontend documentation
    ├── public/
    │   ├── index.html         # HTML template
    │   ├── manifest.json      # PWA manifest
    │   └── robots.txt         # SEO robots file
    └── src/
        ├── App.js             # Main React component
        ├── index.js           # React entry point
        └── index.css          # Global styles

---

Database Schema

Tables

1. companies

Column	Type	Description
id	Integer (PK)	Unique identifier
name	String	Company name
industry	String	Industry sector
credit_score	Float	0-100 credit score
credit_rating	String	Letter rating (AAA-B-)
risk_level	String	Low/Medium/High
created_at	DateTime	Creation timestamp
updated_at	DateTime	Last update timestamp

2. financial_statements

Column	Type	Description
id	Integer (PK)	Unique identifier
company_id	Integer (FK)	Reference to company
statement_date	Date	Statement period end date
period_type	String	Q1/Q2/Q3/Q4/Annual
total_assets	Float	Total assets
current_assets	Float	Current assets
total_liabilities	Float	Total liabilities
current_liabilities	Float	Current liabilities
total_debt	Float	Total debt
shareholders_equity	Float	Shareholders' equity
revenue	Float	Total revenue
operating_expenses	Float	Operating expenses
ebitda	Float	EBITDA
interest_expense	Float	Interest expense
net_income	Float	Net income
current_ratio	Float	Calculated ratio
debt_to_equity	Float	Calculated ratio
debt_to_ebitda	Float	Calculated ratio
interest_coverage	Float	Calculated ratio
profit_margin	Float	Calculated %
ebitda_margin	Float	Calculated %
roe	Float	Calculated %

3. covenants

Column	Type	Description
id	Integer (PK)	Unique identifier
company_id	Integer (FK)	Reference to company
covenant_type	String	Type of covenant
threshold	Float	Maximum/minimum value
current_value	Float	Current actual value
status	String	Compliant/Warning/Breach
last_checked	DateTime	Last check timestamp

---

Credit Scoring Methodology

Score Components

The credit score (0-100) is calculated using four weighted components:

1. Liquidity Score (20% weight)

Based on Current Ratio:

• ≥ 2.0: 100 points (Excellent)
• ≥ 1.5: 80 points (Good)
• ≥ 1.0: 60 points (Adequate)
• ≥ 0.75: 40 points (Weak)
• < 0.75: 20 points (Poor)

2. Leverage Score (30% weight)

Based on Debt/Equity and Debt/EBITDA:

• Debt/Equity ≤ 0.5: 100 points
• Debt/Equity ≤ 1.0: 80 points
• Debt/Equity ≤ 2.0: 60 points
• Debt/Equity ≤ 3.0: 40 points
• Debt/Equity > 3.0: 20 points

3. Profitability Score (25% weight)

Based on Profit Margin and ROE:

• Profit Margin ≥ 20%: 100 points
• Profit Margin ≥ 10%: 80 points
• Profit Margin ≥ 5%: 60 points
• Profit Margin ≥ 0%: 40 points
• Profit Margin < 0%: 20 points

4. Coverage Score (25% weight)

Based on Interest Coverage Ratio:

• ≥ 10x: 100 points (Excellent)
• ≥ 5x: 80 points (Good)
• ≥ 3x: 60 points (Adequate)
• ≥ 1.5x: 40 points (Weak)
• < 1.5x: 20 points (Poor)

Overall Credit Score Calculation

Credit Score = (Liquidity × 0.20) + (Leverage × 0.30) + 
               (Profitability × 0.25) + (Coverage × 0.25)

Credit Rating Assignment

• 90-100: AAA (Exceptional)
• 85-89: AA+ (Excellent)
• 80-84: AA (Excellent)
• 75-79: AA- (Very Good)
• 70-74: A+ (Good)
• 65-69: A (Good)
• 60-64: A- (Adequate)
• 55-59: BBB+ (Moderate)
• 50-54: BBB (Moderate)
• 45-49: BBB- (Acceptable)
• 40-44: BB+ (Speculative)
• 35-39: BB (Speculative)
• 30-34: BB- (High Risk)
• 25-29: B+ (Very High Risk)
• 20-24: B (Very High Risk)
• < 20: B- (Default Risk)

Risk Levels

• Low Risk: Credit Score ≥ 70
• Medium Risk: Credit Score 50-69
• High Risk: Credit Score < 50

Lending Recommendations

• Approve: Credit Score ≥ 70
• Review: Credit Score 50-69
• Reject: Credit Score < 50

---

Troubleshooting

Common Issues

Issue 1: "Module not found" errors

Solution:

# Make sure virtual environment is activated
source venv/bin/activate  # macOS/Linux
venv\Scripts\activate     # Windows

# Reinstall dependencies
pip install -r requirements.txt

Issue 2: CORS errors in browser

Solution: The backend includes CORS support. Ensure:

1. Backend is running on port 5000
2. Frontend is configured to use http://localhost:5000/api
3. Flask-CORS is installed: pip install Flask-CORS

Issue 3: Database not created

Solution:

# Delete existing database
rm credittheory.db

# Restart Flask application
python app.py

The database will be automatically recreated with seed data.

Issue 4: Port already in use

Solution:

# Find process using port 5000
lsof -ti:5000  # macOS/Linux
netstat -ano | findstr :5000  # Windows

# Kill the process
kill -9 <PID>  # macOS/Linux
taskkill /PID <PID> /F  # Windows

# Or use a different port
flask run --port 5001

Issue 5: Frontend can't connect to backend

Solution:

1. Check backend is running: Visit http://localhost:5000/api/health
2. Verify API_BASE_URL in frontend matches backend URL
3. Check browser console for CORS errors
4. Ensure Flask-CORS is enabled in backend

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Next Steps

Production Deployment

1. Replace SQLite with PostgreSQL or MySQL
2. Set up environment variables for configuration
3. Implement authentication and authorization
4. Add SSL/TLS certificates
5. Set up monitoring and logging
6. Configure reverse proxy (nginx/Apache)

Feature Enhancements

1. Add user authentication and role-based access
2. Implement email alerts for covenant breaches
3. Add export to PDF/Excel functionality
4. Build detailed reporting modules
5. Integrate with external data sources (SEC EDGAR, market data)
6. Add predictive analytics and trends
7. Implement audit trails

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📝 License

This project is for educational and demonstration purposes.

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Support

For issues or questions:

1. Check the troubleshooting section
2. Review API documentation
3. Check Flask and SQLAlchemy documentation

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Getting Started Quick Reference

# 1. Set up environment
python3 -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run the application
python app.py

# 4. Test the API
curl http://localhost:5000/api/health
curl http://localhost:5000/api/companies

That's it! credittheory is now running and ready for credit analysis!