SecureFlow: Real-Time Fraud Detection System

SecureFlow is a comprehensive, real-time transaction monitoring and fraud detection system. It combines a robust Machine Learning pipeline with a modern web dashboard to identify and visualize potentially fraudulent activity.

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🚀 Overview

SecureFlow uses an advanced Gradient Boosting / Random Forest model trained on large datasets to predict the likelihood of fraud for financial transactions. It provides a real-time API for external integrations and a sleek frontend for monitoring transactions, visualizing trends, and managing security alerts.

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🏗️ Project Structure

The project is divided into two main components:

• model/: Contains the Machine Learning logic, including model training scripts, the pre-trained pipeline (.joblib), and a FastAPI server for real-time predictions.
• frontend/: A modern Next.js 15 web application providing a dashboard for transaction monitoring, authentication, and reporting.
• Architecture Diagrams: See architecture.puml, frontend.puml, and backend.puml for detailed system designs.

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🛠️ Tech Stack

ML Backend

• Language: Python 3.9+
• API Framework: FastAPI (Real-time serving)
• ML Libraries: Scikit-learn, Pandas, imbalanced-learn
• Serialization: Joblib
• Database/Search: MongoDB (via mongodb-mcp-server)

Frontend Dashboard

• Framework: Next.js 15
• Styling: Tailwind CSS
• Authentication: Clerk
• Payments: Stripe
• Charts: Recharts
• State Management: Zustand
• UI Components: Radix UI, Lucide React, Sonner

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⚙️ Getting Started

📦 Prerequisites

• Node.js (v18.x or later)
• Python (v3.9 or later)
• npm or yarn

1. ML Backend Setup (FastAPI)

Navigate to the model/ directory and set up the environment:

cd model
# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the API server
uvicorn api:app --reload --port 8000

• The API will be available at: http://localhost:8000
• Swagger Documentation: http://localhost:8000/docs

2. Frontend Setup (Next.js)

Navigate to the frontend/ directory and set up the dashboard:

cd frontend
# Install dependencies
npm install

# Run the development server
npm run dev

• The dashboard will be available at: http://localhost:3000

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🤖 Model Information

The core of SecureFlow is a pre-trained machine learning pipeline:

• Inputs: Transaction details (step, type, amount, old/new balances).
• Logic: Predicts isFraud (0 or 1) and calculates a probability score.
• Pipeline: Includes feature engineering for balance errors (errorBalanceOrig, etc.) to enhance detection accuracy.

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🔭 Architecture

The system follows a decoupled architecture where the Next.js frontend interacts with the FastAPI backend over REST. Visual representations can be found in the root directory as .puml files, which can be rendered using PlantUML.