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:

Tool	What it does
get_company_financials	Pulls live balance sheet & income statement data for any stock ticker (via yfinance)
calculate_altman_zscore	Computes the Altman Z-Score — a classic bankruptcy-risk formula combining 5 financial ratios — for a public company
predict_loan_default_risk	Predicts an individual loan applicant's default probability using a logistic regression model

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.

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