Financial Fraud Detection

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

Dashboard — MCP server connected, tools & prompts discovered	Full fraud risk report — HIGH risk, 7 accounts flagged
Executive summary with rule-based + statistical findings	Structuring / smurfing accounts (A1003, A1009)
Velocity abuse — Account A1007, 6 transactions in 3 minutes	Plain-English summary for non-technical executives
Account A1003 deep dive — structuring legal analysis	Auto-generated compliance escalation email
Claude honestly explaining what its tools can and can't determine

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_report

Claude 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 — FastMCP server + stdio_client
• Anthropic 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.py

Open http://localhost:7860 in your browser.

To use the terminal client instead:

python client.py

Sample 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

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Built by Archana Gurimitkala · Powered by Claude Opus 4.8 + MCP