financial-fraud-detection-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@financial-fraud-detection-mcpGive me a full fraud risk report"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Financial Fraud Detection — MCP Demo
An AI-powered financial fraud detection system built with Model Context Protocol (MCP) and Claude Opus 4.8. Features a dark-themed Gradio dashboard where Claude autonomously calls fraud detection tools via MCP.
Screenshots
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Dashboard — MCP server connected, tools & prompts discovered | Full fraud risk report — HIGH risk, 7 accounts flagged |
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Executive summary with rule-based + statistical findings | Structuring / smurfing accounts (A1003, A1009) |
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Velocity abuse — Account A1007, 6 transactions in 3 minutes | Plain-English summary for non-technical executives |
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Account A1003 deep dive — structuring legal analysis | Auto-generated compliance escalation email |
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Claude honestly explaining what its tools can and can't determine |
Related MCP server: Financial Intelligence MCP Server
What It Does
Analyzes 30 simulated transactions across 11 accounts
Detects fraud using two complementary methods:
Rule-based pattern matching — velocity abuse, duplicate charges, structuring (smurfing)
Statistical anomaly detection — IQR method to surface unusual transaction amounts
Generates plain-English risk reports suitable for compliance officers
Exports the full chat session as a formatted PDF
MCP Architecture
Gradio UI (app.py)
│
└── MCP Client (stdio)
│
└── MCP Server (server.py)
├── Tools (4)
│ ├── analyze_transactions
│ ├── detect_fraud_patterns
│ ├── flag_anomalies
│ └── generate_risk_report
├── Resources (1)
│ └── transactions://sample
└── Prompts (2)
├── fraud_analysis
└── stakeholder_reportClaude receives a user question, autonomously decides which tools to call, executes them via MCP, and synthesizes the results into a final answer — no hardcoded logic in the UI layer.
Fraud Scenarios in Sample Data
Pattern | Accounts | Description |
Velocity Abuse | A1007 | 6 transactions in under 3 minutes ($480 total) |
Duplicate Charges | A1004, A1006 | Identical amount + merchant within 60 seconds |
Structuring / Smurfing | A1003, A1009 | Multiple transactions just under $10,000 (31 U.S.C. § 5324) |
Statistical Anomalies | A1005, A1011 | Amounts exceeding IQR upper bound of ~$17,365 |
Tech Stack
MCP Python SDK —
FastMCPserver +stdio_clientAnthropic Python SDK — Claude Opus 4.8 agentic loop
Gradio — dark dashboard UI
pandas — transaction analysis and IQR statistics
fpdf2 — PDF export
Setup
# Clone the repo
git clone https://github.com/archana-gurimitkala/financial-fraud-detection-mcp.git
cd financial-fraud-detection-mcp
# Install dependencies
pip install -r requirements.txt
# Set your Anthropic API key (the app reads it from the environment)
export ANTHROPIC_API_KEY=your_key_here
# Run the Gradio dashboard
python app.pyOpen http://localhost:7860 in your browser.
To use the terminal client instead:
python client.pySample Questions to Try
"Give me a full fraud risk report"
"Which accounts show structuring patterns?"
"Are there any duplicate transactions?"
"Which account has the highest velocity abuse?"
"Summarize the findings for a non-technical executive"
Sample PDF Output
A full exported chat session is included as sample_output.pdf — 8 pages covering the complete fraud analysis, structuring deep dive, velocity abuse breakdown, executive summary, and compliance escalation email.
Course Context
Built to demonstrate concepts from Anthropic's Introduction to MCP course:
MCP server with Tools, Resources, and Prompts primitives
stdio transport
Agentic tool-use loop (Claude decides when and what to call)
Multi-turn conversation with tool results fed back into context
Built by Archana Gurimitkala · Powered by Claude Opus 4.8 + MCP
This server cannot be deployed
Maintenance
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