Real-Time Fraud Detection Engine

A real-time fraud detection system that streams transactions, detects suspicious activity using ML and Google Gemini, and visualizes alerts instantly via a live dashboard.

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Inspiration

Financial fraud often goes unnoticed until significant damage is already done. Many existing fraud systems rely on batch processing or delayed analysis, which is ineffective for modern, high-volume transaction streams.

We wanted to build a system that detects fraud as transactions happen, provides real-time alerts, and explains why a transaction is suspicious using AI — not just a binary flag.

What it does

Fraud Detection Engine is a real-time fraud detection platform that continuously processes transaction streams, identifies suspicious activity, and pushes instant alerts to a live dashboard.

Key features include:

  • Live transaction ingestion and processing
  • Real-time fraud alerts via WebSockets
  • AI-generated explanations for flagged transactions
  • Live metrics and fraud trend visualization
  • Automatic fallback logic when AI services are unavailable

How I built it

The system is built as an end-to-end streaming architecture:

  • Kafka (Confluent Cloud) streams transaction events in real time
  • FastAPI serves as the backend API and WebSocket server
  • WebSockets push live updates to the frontend without refresh
  • Google Vertex AI (Gemini models) generate human-readable fraud explanations
  • Rule-based + ML scoring determines fraud likelihood
  • HTML, CSS, JavaScript power the real-time dashboard UI
  • Docker orchestrates services for local development

The backend consumes transactions from Kafka, evaluates fraud risk, triggers alerts, and broadcasts updates instantly to connected clients.

Challenges I ran into

  • Real-time synchronization: Keeping REST-based metrics and WebSocket updates consistent without UI flicker or duplicate counts.
  • WebSocket stability: Handling disconnects, reconnects, and connection state flickering in the frontend.
  • Gemini model compatibility: Adapting to evolving Gemini model versions and API availability within Vertex AI.
  • Fault tolerance: Ensuring the system continues working when external AI services fail.
  • Streaming complexity: Managing Kafka consumers and producers reliably in a local development setup.

Each challenge required iterative debugging and architectural adjustments.

Accomplishments that we're proud of

  • Built a fully real-time fraud detection pipeline from ingestion to visualization
  • Implemented live WebSocket updates with stable reconnect handling
  • Designed a graceful AI fallback system when Gemini is unavailable
  • Created a clean, production-style dashboard with live analytics
  • Integrated multiple cloud services into a single cohesive system

What we learned

  • Real-time systems require careful state management across multiple data sources
  • WebSockets introduce edge cases that don’t exist in traditional REST APIs
  • Cloud AI APIs evolve rapidly and must be handled defensively
  • Observability and logging are critical in streaming architectures
  • Building resilient systems matters more than perfect models

What's next for Real-Time Fraud Detection Engine

  • Deploying the system on cloud infrastructure for scalability
  • Adding adaptive fraud thresholds based on behavior patterns
  • Supporting multiple AI models for explanation comparison
  • Introducing role-based dashboards for analysts and admins
  • Expanding detection beyond transactions (logins, behavior, anomalies)

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