Massive Web Scraping & Classification: Detecting Online Payment Acceptance Across 200M Sites

Introduction

In today's digital landscape, businesses need precise insights into online payment acceptance trends. Retail Scrape, a leader in enterprise web crawling services, developed an advanced web scraping and classification system to analyze 200 million websites and determine whether they accept VISA, Mastercard, and PayPal. This large-scale data extraction project aimed to provide an accurate database of online businesses, helping a leading payment provider identify potential clients.

Client Requirement

The client, a global online payment provider, sought an automated solution to:

• Scan a database of 200M websites to check for online payment acceptance.
• Classify sites into two categories: those that accept VISA, Mastercard, and PayPal and those that do not.
• Ensure 90% classification accuracy.
• Deliver results within 30 days using a robust scraping infrastructure.
• Provide a stable, automated bot that does not stop or deviate from the task.

Challenges in Web Scraping and Classification

1. Handling 200M Websites Efficiently

The sheer volume of 200 million URLs required a powerful web crawling framework with distributed computing for fast data retrieval.

2. Avoiding Keyword-Based Errors

A primary challenge was accurate classification without relying solely on keyword matching. Many e-commerce sites mention “PayPal” or “VISA” without actually offering these payment options.

3. Managing Website Variability

Websites use diverse payment processing structures, making it challenging to standardize extraction techniques. Some sites embed payment information in JavaScript-heavy interfaces, while others use API-based checkout systems.

4. Preventing Anti-Scraping Measures

Large-scale scraping often triggers CAPTCHAs, IP blocks, and bot detection mechanisms. The project needed a stealthy scraping approach using rotating IPs, user agents, and headless browsers.

5. Ensuring 90% Accuracy

To meet accuracy requirements, Retail Scrape implemented a multi-layered verification process, cross-referencing data with actual payment transactions where possible.

Solution: Retail Scrape’s Web Crawling & AI-Powered Classification System

1. High-Performance Web Scraping Framework

Retail Scrape built a scalable scraping architecture using a combination of:

• Distributed crawlers powered by Scrapy, Selenium, and Puppeteer.
• Cloud-based data processing (AWS, GCP, and Azure) for high-speed requests.
• Rotating IPs and anti-bot detection mechanisms to ensure uninterrupted crawling.

2. AI-Based Classification for Payment Detection

Rather than relying on keyword searches, we implemented an AI-powered classification model using:

• Natural Language Processing (NLP) to analyze checkout pages and payment policies.
• Computer Vision Algorithms to scan payment logos on checkout pages.
• Machine Learning Models trained on labeled datasets to identify actual payment acceptance.

3. Real-Time Data Validation

To ensure 90% accuracy, the extracted data was cross-verified using:

• Transactional history checks from test payments.
• API integration with payment gateways for verification.
• Manual auditing for a random sample of sites to fine-tune the model.

4. Automation & Deployment

The entire system was deployed on a dedicated server with:

• Automated data extraction scripts running 24/7.
• Failover mechanisms to prevent downtimes.
• Encrypted data transfer to ensure security and compliance.

Implementation & Results

Phase 1: Development (15 Days)

• Created custom scraping bots.
• Trained machine learning models for accurate classification.
• Implemented IP rotation & CAPTCHA bypass techniques.

Phase 2: Execution (30 Days)

• Scanned 200M websites within the given timeframe.
• Classified sites into two lists:
• Accepted Payments: Sites supporting VISA, Mastercard, PayPal.
• No Payment Acceptance: Sites without online payment options.
• Ensured accuracy compliance, with less than 20 misclassified sites per 200 results.

Final Results & Delivery

• Successfully delivered a structured dataset with high classification accuracy.
• Enabled the client to identify thousands of potential leads for their online payment services.
• Provided a comprehensive report & deployment instructions for future use.

Key Benefits for the Client

• Enhanced Lead Generation: Identified businesses needing payment services.
• Competitive Market Insights: Gained valuable data on global payment trends.
• Scalable & Reusable Model: The system can be re-run periodically to update insights.
• Cost-Effective Solution: Saved significant manual labor and research costs.

Future Enhancements

To further improve performance, Retail Scrape is working on:

• Expanding the database to include more payment methods like Apple Pay, Google Pay, and cryptocurrencies.
• Real-time scraping solutions for continuous market monitoring.
• Integrating AI chatbots to interact with websites and confirm payment options dynamically.

Conclusion

Retail Scrape’s advanced web scraping and AI-based classification system successfully analyzed 200 million websites, delivering high-accuracy results in a record 30-day timeframe. This case study highlights the power of automated data extraction in identifying payment trends and unlocking new business opportunities for payment service providers.