Credit Risk Analytics MCP Server

A Python MCP (Model Context Protocol) server that exposes credit-risk analytics as callable tools for Claude Desktop — turning natural-language questions into real financial risk calculations.

What it does

This server gives Claude three tools:

Ask Claude Desktop something like "What's the Altman Z-Score for TCS.NS?" or give it a loan applicant's income, debt ratio, and credit history, and it calls the right tool, runs the real calculation, and explains the result.

Related MCP server: Finance MCP Server

Why MCP

Without MCP, these would just be Python functions you'd have to run yourself. MCP turns them into tools an AI client can call directly: Claude Desktop sends a structured JSON-RPC request to this server, the server runs the actual calculation, and sends the result back — so you get a live, verifiable answer instead of a guess from the model's training data.

Project structure

credit-risk-mcp/
├── server.py          # The MCP server — defines all 3 tools
├── train_model.py      # Generates synthetic credit data + trains the logistic regression model
├── requirements.txt    # Python dependencies
├── model.pkl            # Trained logistic regression model
├── scaler.pkl            # StandardScaler used to preprocess model inputs
└── .gitignore

Setup

git clone https://github.com/Lipika118/credit-risk-mcp.git
cd credit-risk-mcp
python3 -m venv venv
source venv/bin/activate      # Windows: venv\Scripts\activate
pip install -r requirements.txt

The trained model (model.pkl, scaler.pkl) is already included, so you can skip straight to running the server. If you want to retrain it yourself:

python3 train_model.py

This generates a synthetic-but-realistic applicant dataset (income, debt ratio, credit history, late payments, loan amount, age), trains a logistic regression model, and prints the test AUC.

Testing standalone

Before connecting to Claude Desktop, test the tools directly with the MCP Inspector:

pip install "mcp[cli]"
mcp dev server.py

This opens a browser UI where you can call each tool manually and see the JSON-RPC request/response for each one.

Connecting to Claude Desktop

Add this to your claude_desktop_config.json (%APPDATA%\Claude\claude_desktop_config.json on Windows, ~/Library/Application Support/Claude/claude_desktop_config.json on Mac):

{
  "mcpServers": {
    "credit-risk": {
      "command": "/full/path/to/venv/Scripts/python.exe",
      "args": ["/full/path/to/credit-risk-mcp/server.py"]
    }
  }
}

Fully quit and reopen Claude Desktop, then check Connectors in the chat input menu — credit-risk should be listed and toggled on.

Example usage

Company risk:

"What's the Altman Z-Score for TCS.NS?"

Ticker: TCS.NS
Altman Z-Score: 10.69
Risk Zone: Safe zone (low bankruptcy risk)

Component ratios:
  Working Capital / Total Assets: 0.410
  Retained Earnings / Total Assets: 0.548
  EBIT / Total Assets: 0.366
  Market Cap / Total Liabilities: 11.271
  Revenue / Total Assets: ...

Individual risk:

"A loan applicant has monthly income 40000, debt-to-income ratio 0.5, 3 years credit history, 2 late payments last year, wants a loan of 250000, and is 27 — what's their default risk?"

Default probability: 78.0%
Risk band: High risk

Notes on the model

The loan-default model is trained on synthetic data, not real applicant records — this avoids privacy/licensing issues while still learning genuine, explainable relationships (higher debt-to-income ratio and more late payments both increase predicted default risk). It's meant to demonstrate the MCP integration pattern, not to be used for real lending decisions.

Safety

predict_loan_default_risk and calculate_altman_zscore are both read-only — they don't modify any data or make external calls beyond fetching public market data.

License

MIT