FraudShield AI – Real-time Fraud Detection System

Concept:
A production-ready, Dockerized fraud detection system that uses machine learning and business rules to detect fraudulent transactions with high accuracy and low-latency inference.

Problem Solved:
Enables real-time fraud detection using an ensemble ML model, risk scoring, contextual business rules, and analytics dashboards. Helps reduce fraud losses and improves decision-making for financial systems.

Scope:

• Input:
  Transaction data (amount, balance, transaction count, location flags, card age, fraud history, etc.).

• Process:
  A fully containerized microservice architecture:

  1. Frontend (Gradio UI):
     User-facing interface for live predictions, analytics, and dashboards.

  2. Backend (FastAPI Server):
     Handles prediction requests, business rules, analytics endpoints, and retraining.

  3. Machine Learning Engine:
     Ensemble model (Random Forest + Logistic Regression) performing real-time inference with <50ms model latency.

  4. Business Rules Layer:
     Additional fraud heuristics layered on top of model output.

  5. Health & Monitoring:
     Auto-recovery, service discovery, and logging through Docker.

• Output:
  Real-time fraud predictions (<100ms), risk scores, classification levels (Low/Medium/High), dashboards, model analytics, and access to OpenAPI documentation.

Features:

• 98% accuracy on extreme fraud cases

• Risk scoring + confidence levels

• Real-time predictions

• Feature importance visualization

• Dashboard with transaction statistics

• REST API with documented endpoints

• Retraining endpoint

• Health checks

• Microservices with Docker Compose

• Persistent storage through volumes

Tech Stack:

• Backend: FastAPI

• Frontend: Gradio

• Machine Learning: Scikit-learn (Random Forest, Logistic Regression)

• Data: CSV, Pandas, NumPy

• Infrastructure: Docker, Docker Compose

• Logs & Monitoring: FastAPI logging + container logs

• Output: JSON, UI dashboard

Project Structure:
(High-level)

• backend/ — FastAPI app, ML models, database layer

• frontend/ — Gradio UI

• models/ — Saved ML models

• data/ — Training + live data

• docker-compose.yml — Orchestration layer

• requirements.txt — Dependencies

API Endpoints:

• /predict — Single prediction

• /batch-predict — Batch predictions

• /train — Retrains the model

• /health — System health

• /model-info — Metadata + performance

• /feature-importance — Feature rankings

• /stats — Dashboard stats

• /transactions — Recent transactions

Performance Metrics:

• <100ms API response

• <50ms model inference

• 98% extreme fraud detection

• 2s UI load time

• 30s container startup

Achievements:

• Fully Dockerized ML system

• Stage-ready fraud detection workflow

• Accurate ensemble model

• Real-time API + analytics

• Extendable microservice architecture