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financial-fraud-detection-mcp

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

Dashboard

Risk Report

Dashboard — MCP server connected, tools & prompts discovered

Full fraud risk report — HIGH risk, 7 accounts flagged

Executive Summary

Structuring

Executive summary with rule-based + statistical findings

Structuring / smurfing accounts (A1003, A1009)

Velocity

Non-Technical

Velocity abuse — Account A1007, 6 transactions in 3 minutes

Plain-English summary for non-technical executives

Deep Dive

Email

Account A1003 deep dive — structuring legal analysis

Auto-generated compliance escalation email

Clean

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_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

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


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

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