Payment-Risk-Exception-AI-Agent

A local application example that demonstrates a hybrid payment-decisioning workflow. A rule based deterministic decision policy first assesess transactions and automatically approves or denies. The transactions that have mismatched info but not severe enough to trigger a denial is routed to an LLM agent which reviews the transactions then approves/denies or sends for human-review.

This example uses the following:

• Stripe's official Python SDK (stripe-python)
• Stripe's official mock server (stripe-mock)
• Rule based deterministic decision policy
• Simulated Merchant Data
• LLM Agent
• MCP Server with discoverable tools
• FastAPI REST endpoints
• SQLite
• Docker
• Postman API testing
• Analyst dashboard

The four demo transactions

1. Everything matches
2. Travel mismatch
3. Compounding risk
4. Mixed evidence

LLM Agent MCP flow

For exception transactions, the model itself selects the tools:

LLM
  └─ calls get_transaction_context through MCP
       └─ receives Stripe + merchant context and deterministic signals
  └─ then calls exactly one MCP terminal tool:
       ├─ record_ai_decision (approve or deny)
       └─ create_human_review

The Python orchestrator executes the tool calls requested by the model. It does not silently replace the model with a deterministic fallback and does not choose the terminal MCP action on the model's behalf.

Setup

For testing, please create an .env file and provide the LLM key.

Run:

docker compose up --build

Open:

• Dashboard: http://localhost:8000
• Health/API configuration: http://localhost:8000/api/health
• FastAPI docs: http://localhost:8000/docs
• MCP endpoint: http://localhost:8001/mcp
• Stripe Mock: http://localhost:12111

Important boundaries

• Stripe Mock returns fixture-based Stripe objects. It does not process money, run Stripe risk models, or preserve a real payment lifecycle.
• Merchant session and account data are simulated demo data.
• Clean transactions stay on deterministic policy; the model is called only for exceptions.
• The MCP server allows context retrieval, AI decision persistence, and human-review creation.
• Human-review resolution is intentionally absent from MCP. Only the analyst REST/dashboard path can resolve it.

Screenshots:

Main Screen:

Rules Fired:

AI Explanation:

Transaction Details:

Video Preview:

https://github.com/user-attachments/assets/84ff0226-a9cb-4c60-a4f7-ed301f0b5f5d