How to Use Web Scraping to Detect Fraud

Web scraping solutions are often viewed negatively in the absence of legal and technological information in this area.

Contrary to unpopular opinion, data scraping techniques using automated bots to visit and extract data from the internet are becoming increasingly important.

Web scraping, like any other technology, can be used for both constructive and destructive purposes – depending on how you use it. Scrapers can be used to detect fraudulent activity online, providing much-needed insights to track and stop these fraudulent actions in a timely manner.

Here's how a data scraping project can help you monitor websites for such instances.

Why Scraping?

Scraping is incredibly fast and powerful. When it comes to monitoring web pages for content, using humans is an inefficient and impractical approach.

With scrapers, you can monitor hundreds of thousands of web pages and receive notifications when fraudulent activity is detected. The speed and scale at which scraping operates make it the best solution for fraud detection.

Applications of Scrapers for Fraud Detection

1. TOS Compliance Monitoring 

If you have partner sites that need to be monitored for compliance with your company's terms of service, scraping is the best way. For example, if you are a company that provides advertising placement services for online publishers, you may want to ensure that these sites publish content in accordance with your rules.

You may want to receive notifications if publishers publish questionable content. This can be done by setting up scraping to search for blacklisted keywords on publisher sites. TOS compliance monitoring is a great use case for scraping that helps avoid policy violations and fraud.

2. Detection of Illegal Goods Sales

You can monitor the sale of illegal goods on partner sites using scraping. If you provide payment gateway services used by a large number of websites, you may want to ensure that they use your service to sell legal and authentic goods.

To track illegal goods sales, you can set up a monitoring system using scraping. This scraper can be programmed to search for keywords related to restricted product categories or illegal goods. Thus, scanning websites will protect your business from fraudulent activities by partners.

price scraping

3. Monitor Copyright Infringements

Copyright infringement takes many forms online. From text content to movies and music, nothing is exempt from illegal copying and reproduction.

You can use scraping to scan websites for content that belongs to you. This can be a one-time or ongoing activity depending on the expected frequency of copyright infringement. With scraping, scanning a vast number of websites for plagiarism is fast and efficient.

How to Scrape Websites for Fraud Detection

The data collection setup must be manually programmed for each specific use case. This is because every website on the internet differs in structure and design. For fraud detection, you need to provide the web crawler with a list of URLs to monitor and a list of keywords or code snippets to search for.

Keywords can include text that is associated with potential violations you expect.

Pages with illegal goods for sale can be tracked by using the names of those goods as keywords in the scraper settings. In this case, it is important to use a huge list of keywords with all possible variations to increase the effectiveness of the setup.

Web pages with malware can be detected by configuring the scraper to search for code snippets commonly used for phishing, malware injection, or clickjacking.

Conclusion

Ironically, a technology that has tremendous application in fraud detection is itself considered something unethical or illegal.

Web scraping can help detect fraudulent activities such as illegal sales of goods, plagiarism, and even fraud itself. This underscores the fact that there are no bad technologies, only bad people. Thus, when applied correctly, scraping technologies will continue to help businesses across various verticals succeed through the power of deep data.