FraudShield Graph AI

A graph-based fraud detection system for finance and e-commerce, leveraging ArangoDB, cuGraph, and GPT-4o to identify suspicious transaction patterns, detect anomalies, and prevent fraud in real time.

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Inspiration

Fraudulent transactions in finance and e-commerce have led to significant financial losses. Traditional fraud detection methods struggle with complex patterns, making graph-based AI an innovative solution.

What it does

FraudShield Graph AI analyzes financial transactions using graph-based analytics and AI to detect fraudulent behavior. It identifies suspicious activity, unusual patterns, and connections between fraudulent accounts, providing real-time alerts to administrators via Telegram.

How we built it

  • Database: ArangoDB for storing financial transaction data in a graph structure.
  • Graph Analysis: NetworkX and cuGraph for fraud pattern detection. A sample dataset (G_nx = nx.karate_club_graph()) was used to demonstrate how fraud detection can be performed using graph analytics.
  • AI Integration: GPT-4o via LangChain for querying and interpreting fraud risks.
  • Frontend & Alerts: Telegram bot and a web-based UI for real-time monitoring.

Challenges we ran into

  • Data Complexity: Graph relationships can be vast, requiring optimized query performance.
  • Scalability: Processing large-scale financial transactions efficiently.
  • Real-time Processing: Balancing AI-based analysis with speed for immediate fraud alerts.

Accomplishments that we're proud of

  • Successfully integrating graph-based fraud detection with real-time AI insights.
  • Implementing GPU-accelerated fraud analysis for efficiency.
  • Building a Telegram bot to notify admins about detected fraud cases.

What we learned

  • How to leverage cuGraph for large-scale fraud detection.
  • Optimizing ArangoDB queries for quick retrieval and processing.
  • AI-driven anomaly detection can significantly enhance traditional fraud prevention systems.

What's next for FraudShield Graph AI

  • Expanding integrations with financial institutions and e-commerce platforms.
  • Enhancing AI models to improve fraud prediction accuracy.
  • Adding a web dashboard for deeper fraud case analysis and reporting.
  • Improving real-time processing with further GPU optimizations.

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