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AI Fraud Detection for Ticket Scalping

Objective

To prevent ticket scalping (bulk buying of tickets for resale at inflated prices) using AI and data analytics by detecting suspicious behavior patterns, flagging fake accounts, and enforcing real-time fraud prevention measures during ticket sales.

Why Choose This Project?

  • Ticket scalping leads to inflated prices, unfair access, and platform misuse

  • Showcases AI application in fraud detection

  • Valuable for event organizers, ticketing portals, travel booking apps

  • Great addition to cybersecurity, AI, or data science portfolios

Key Features

Feature Description
Behavioral Analysis Detects abnormal user behavior such as very fast bookings or same-user devices
IP & Device Fingerprinting Tracks multiple purchases from the same IP/device despite different accounts
Machine Learning Models Predicts scalping behavior using logistic regression, decision trees, or deep learning
Bot Detection Identifies and blocks automated bots trying to buy tickets in bulk
Risk Scoring System Assigns fraud score to each transaction in real time
Admin Dashboard Visual interface to view flagged users, trends, and fraud analytics
Alerts & Blocking Real-time alerts to admins + automatic blocking of high-risk users

Workflow

  1. User logs in and attempts to book tickets

  2. The system captures data: IP address, device ID, speed of clicks, account age, etc.

  3. ML model evaluates risk score based on training data

  4. If score > threshold, booking is flagged or blocked

  5. Admin reviews flagged accounts from dashboard

  6. Feedback loop continuously trains model to improve accuracy

Technology Stack

Layer Tech Stack Options
Frontend HTML, CSS, Bootstrap, JavaScript
Backend Python (Flask/Django) / Node.js / Java (Spring Boot)
Machine Learning Scikit-learn / TensorFlow / PyTorch
Data Storage MySQL / PostgreSQL / MongoDB
Real-time Analytics Apache Kafka / Apache Spark (optional for high volume)
Bot Prevention CAPTCHA, rate limiting, and browser fingerprinting

Machine Learning Approach

Step Detail
Data Collection Historical ticket purchase logs, user behavior, fraud cases
Feature Engineering Features like: # of tickets, speed of action, time of day, IP history
Model Training Logistic Regression, Random Forest, XGBoost or LSTM
Evaluation Accuracy, ROC-AUC, Precision/Recall metrics
Deployment Model exposed as REST API for real-time scoring

Security Measures

  • CAPTCHA to prevent bots

  • Rate-limiting APIs per user/device

  • OTP/email verification for new logins

  • Audit logs of flagged and blocked attempts

  • IP blacklisting and suspicious region detection

Visualization (Admin Panel)

  • Heatmap of risky locations/IPs

  • Daily/weekly fraud trends

  • List of flagged accounts and their fraud score

  • Exportable reports (CSV, PDF)

Modules

  1. User Module – Secure login, ticket booking

  2. ML Detection Engine – Evaluates booking attempts

  3. Admin Module – View flagged data, manage fraud cases

  4. Data Ingestion Module – Logs user interaction, stores for model input

  5. Feedback & Retraining Module – Uses admin feedback to retrain model

Extendable Ideas

  • Integrate with blockchain for tamper-proof booking records

  • Add user reputation system

  • Use geolocation + real-time face ID for sensitive bookings

  • Connect to national ID databases for strict verification

This Course Fee:

₹ 1599 /-

Project includes:
  • Customization Icon Customization Fully
  • Security Icon Security High
  • Speed Icon Performance Fast
  • Updates Icon Future Updates Free
  • Users Icon Total Buyers 500+
  • Support Icon Support Lifetime
Secure Payment:
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