🐴 GraphMule - Financial Crime Detection Engine

RIFT 2026 Hackathon Finalist Project

GraphMule.mp4

GraphMule is a next-generation, graph-based financial crime detection engine designed to identify money muling networks, smurfing patterns, and shell accounts using advanced network analysis and beautiful, high-performance data visualizations.

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🎯 The Problem

In modern digital finance, money laundering structurally mimics normal transactional traffic. Traditional rule-based engines (e.g., "flag transactions over $10,000") fail to catch coordinated bad actors who break their movements into micro-transactions sent across dozens of burner accounts ("smurfing") before consolidating the funds elsewhere.

💡 The GraphMule Solution

GraphMule analyzes the relationships between accounts, not just the isolated transactions. By representing the financial ledger as a mathematical Graph (where Accounts = Nodes and Transactions = Edges), we employ powerful topological algorithms to instantly detect:

1. Circular Fast-Flows (Money Muling Rings): Funds moving rapidly in closed loops to obfuscate the original source.
2. Scatter-Gather (Smurfing): A single source sending micro-payments to multiple intermediate nodes, which then forward it to a final destination.
3. High-Risk Centrality: Accounts acting as unnatural routing hubs for thousands of separate micro-transactions.

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✨ Core Features

• Advanced Graph Algorithms: Powered by Python's NetworkX library to execute deeply nested Cycle Detection and connected-component analysis on raw transaction ledgers.
• Premium Fintech Aesthetic: A stunning UI/UX designed with a charcoal/graphite color palette, sophisticated glassmorphism, and a liquid 3D mesh landing background using react-three-fiber and ShaderGradients.
• Interactive Threat Modeling: A dynamic, physics-based 2D force graph that allows investigators to visually isolate fraud rings, interrogate individual suspicious accounts, and track relationships in real-time.
• Intelligent Dashboard: At-a-glance summaries calculating total financial risk exposure, flagged networks, and a precise CSV schema modal for clean data ingestion.
• One-Click Intelligence Export: Instantly export the analysis result as a structured JSON report for downstream compliance teams.

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🛠️ Technology Stack

GraphMule perfectly decouples high-performance mathematical modeling from premium client-side rendering.

Frontend (Client)

• Framework: Next.js 14 (React)
• Styling: TailwindCSS
• Visual Data Enging: react-force-graph-2d
• Animations & 3D: Framer Motion, @shadergradient/react, @react-three/fiber, three.js
• Icons: Lucide-React

Backend (API & Analysis)

• Framework: FastAPI (Python)
• Data Processing: Pandas
• Graph Mathematics: NetworkX
• Server: Uvicorn

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🚀 Quick Start & Local Setup

Since this is a decoupled full-stack application, you need to run the backend and frontend separately.

Prerequisites

• Python 3.9+
• Node.js 18+

1. Backend Setup (FastAPI / NetworkX)

Navigate to the backend directory:

cd backend

Create a virtual environment and install dependencies:

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate
pip install -r requirements.txt

Run the intelligence server:

uvicorn main:app --reload --host 0.0.0.0 --port 8000

The API is now analyzing data on http://localhost:8000

2. Frontend Setup (Next.js)

Open a new terminal and navigate to the frontend directory:

cd frontend

Install dependencies:

# We use legacy-peer-deps to allow React 18 compatibility with ShaderGradient
npm install --legacy-peer-deps

Run the development server:

npm run dev

The Web UI is now live on http://localhost:3000

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Built with precision for the RIFT 2026 Hackathon.