AI-Based Ticket Scalping Prevention System

Overview

An AI-based fraud detection and dynamic pricing system for ticketing platforms. Anomaly detection models identify bot activity and suspicious transactions, while pricing and fraud-prevention mechanisms work together for fairer ticket distribution — with CI/CD workflows automating model retraining, testing and deployment to production.

Purpose — Why This Project Exists

Bots can buy up high-demand event tickets within seconds of release, leaving genuine fans to pay inflated resale prices. This system exists to spot that bot-like purchasing behavior in real time — abnormal transaction velocity, patterns and account signals — and respond with fraud blocking and dynamic pricing defenses instead of letting scalpers walk away with the entire inventory.

Animated Pipeline

1

Purchase Event

A ticket purchase attempt comes through.

2

Behavior Features

Transaction velocity, patterns and signals extracted.

3

Anomaly Detection

Model scores the transaction for bot-like behavior.

4

Fraud Scoring

Suspicious transactions flagged for action.

5

Pricing / Blocking

Dynamic pricing and prevention mechanisms applied.

6

Automated Retraining

CI/CD pipeline retrains and redeploys the model.

Key Features

Bot Detection — identifies bot activity and suspicious purchase transactions.

Anomaly Detection Models — machine learning models flag abnormal purchasing patterns.

Dynamic Pricing & Prevention — pricing and fraud-prevention mechanisms for fair distribution.

CI/CD Automation — GitHub Actions automate model retraining, testing and deployment.

Benefits

Fairer Ticket Access

Keeps inventory available to genuine buyers, not bots.

Automated Blocking

Suspicious transactions get flagged without manual review.

Continuously Retrained

CI/CD keeps the fraud model current as bot tactics evolve.

Technology Stack

Python Scikit-learn Anomaly Detection GitHub Actions

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