Online fraud costs e-commerce businesses billions of dollars every year. As more customers shop online, fraudsters continue to develop new ways to exploit payment systems, customer accounts, promotions, and return policies.
For e-commerce businesses, fraud prevention is no longer limited to checking individual transactions. Companies increasingly need to monitor patterns, identify suspicious activity, and use data from multiple sources to understand potential threats.
Web scraping can support this process by helping businesses collect publicly available data from websites, marketplaces, forums, review platforms, and other online sources. This external data can provide additional context for identifying suspicious patterns and protecting revenue.
In this guide, we will explore how web scraping can support e-commerce fraud prevention, what types of data businesses can monitor, and how external data can help strengthen fraud detection strategies.
E-commerce fraud is any deceptive activity intended to steal money, products, personal information, or other valuable resources from an online business or its customers.
Fraud can occur at different stages of the customer journey, including account creation, payment, order fulfillment, returns, and promotional campaigns.
Common examples include:
As fraudsters become more sophisticated, traditional security checks may not always be enough. Businesses need to analyze multiple data points and identify unusual patterns before fraudulent activity causes significant damage. Using structured e-commerce data collection can help businesses monitor online activity, identify unusual patterns, and make better data-driven decisions. Learn more about e-commerce data scraping services and how they can support online business data collection.
Stolen payment card fraud occurs when criminals use compromised credit or debit card information to make unauthorized purchases.
Fraudsters may use stolen card details to purchase high-value products, which are then shipped to different addresses or resold through other online marketplaces.
Account takeover fraud occurs when criminals gain unauthorized access to a customer’s account.
They may use stolen login credentials to:
Monitoring unusual login and transaction behavior can help businesses identify suspicious account activity.
Friendly fraud occurs when a customer makes a legitimate purchase but later falsely claims that the transaction was unauthorized or that the product was never received.
These false chargeback claims can create financial losses for businesses and increase their chargeback rates.
Promotion abuse occurs when users exploit discounts, coupons, referral programs, or other promotional offers.
For example, a user may create multiple accounts to use the same new-customer discount repeatedly.
Triangulation fraud involves multiple parties and often includes stolen payment information.
A fraudster may create a marketplace listing, receive an order, and use stolen payment details to purchase the product from another retailer before shipping it to the customer.
Understanding these different types of fraud helps businesses identify the data and patterns they need to monitor.
Web scraping can support fraud prevention by collecting publicly available information from external sources.
A business may have detailed information about its own transactions, but external data can provide additional context.
For example, a company may monitor:
This external intelligence can be compared with internal business data to identify unusual patterns.
For example, a seller repeatedly offering a company’s products at unusually low prices across multiple marketplaces may require further investigation.
Web scraping does not replace dedicated fraud detection systems. Instead, it can support fraud prevention by providing additional data for analysis and risk assessment.
Fraudsters often leave digital traces across different websites and platforms.
For example, suspicious activity may appear as:
Collecting this publicly available information can help businesses build a broader view of potential threats.
Instead of analyzing only what happens inside an e-commerce store, businesses can also monitor relevant activity across the wider online environment.
Fraud detection becomes more effective when businesses can compare information from different sources.
For example:
Internal Transaction Data
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Marketplace Data
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Public Fraud Reports
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Seller Information
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Broader Risk Analysis
A single data point may not indicate fraud. However, multiple suspicious signals appearing together may justify additional investigation.
Fraud patterns can change quickly.
A suspicious seller may create new listings, change prices, or move to another marketplace within a short period.
Automated data collection can help businesses monitor relevant sources more regularly than manual research.
This allows fraud teams to spend less time collecting information and more time analyzing suspicious activity.
Web scraping can support payment fraud prevention by collecting external information related to publicly reported fraud indicators.
Businesses may use data from legitimate, authorized, and publicly accessible sources to identify information associated with known fraud patterns.
For example, organizations may monitor:
This data can then support internal risk assessment systems.
However, businesses should avoid collecting sensitive personal or financial information unlawfully. Fraud prevention workflows should follow applicable privacy laws, website terms, and data protection requirements.
Public fraud intelligence can include information about newly discovered scams, fraud patterns, and compromised accounts.
Monitoring these sources can help businesses understand how fraud methods are changing.
For example, a company may identify:
This information can help security and fraud teams update their detection rules.
Public online communities may discuss emerging fraud methods and scams.
Monitoring legally accessible public information can help businesses understand new threats.
The goal is not to collect sensitive personal information or facilitate illegal activity. Instead, the purpose is to identify broader fraud trends and use that information to improve defensive systems.
Transaction velocity refers to the frequency and speed of transactions associated with an account, payment method, device, or other identifier.
Unusual activity may include:
When combined with other signals, transaction velocity can help identify automated attacks or suspicious activity.
For example, a sudden series of high-value purchases within a few minutes may require additional verification.
IP intelligence can provide additional context when analyzing online activity.
Businesses may use IP-related data to identify:
This information should not be used as the only reason to block a customer.
Many legitimate users use VPNs, proxies, or shared networks. Therefore, IP information should be combined with other risk signals.
Businesses may compare relevant identifiers against legitimate security and risk databases.
For example, a system may identify whether an IP address or other technical indicator has previously been associated with suspicious activity.
This can help businesses decide whether an order requires additional verification.
VPN and proxy usage is not automatically fraudulent.
However, anonymous network connections may be one of several signals that require additional analysis.
For example, a business may combine network information with:
Using multiple signals provides a more accurate assessment than automatically blocking every VPN user.
Businesses may compare the approximate location of a connection with other transaction details.
For example, an order may require additional review when:
Geolocation should be treated as a supporting signal rather than conclusive proof of fraud.
Fraud can also affect how products are purchased and resold online.
Some fraudulent operations acquire products using stolen payment methods and then resell them through other platforms.
Monitoring marketplaces and other public websites can help businesses identify unusual resale patterns.
For example, businesses may monitor:
This can help identify unusual activity involving a company’s products.
Large differences between a retailer’s price and third-party marketplace prices may require investigation.
For example, a seller consistently offering products at unusually low prices may be using an unauthorized supply source.
Price data alone does not prove fraud. However, it can help businesses identify sellers or listings that deserve closer attention.
Fraudsters may attempt to purchase large quantities of high-demand products.
Monitoring unusual purchasing patterns can help businesses identify:
Businesses can then apply additional verification or review processes when appropriate.
Customer feedback can provide useful information about unusual activity.
Businesses can analyze reviews, ratings, and public discussions to identify patterns that may indicate:
Web scraping can help collect publicly available feedback from multiple platforms for analysis.
Fake reviews may contain similar language, repeated patterns, or unusual timing.
For example, a business may notice that multiple accounts publish very similar reviews within a short period.
Analyzing large volumes of public reviews can help identify patterns that may be difficult to detect manually.
This information can support further investigation and help businesses protect the credibility of their review systems.
Review bombing involves a sudden and coordinated increase in negative reviews or ratings.
Businesses can monitor rating changes over time to identify unusual spikes.
For example:
Normal Rating Activity
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Sudden Increase in Negative Reviews
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Multiple Accounts Posting Similar Feedback
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Investigation
This does not mean every sudden increase in negative feedback is fraudulent. Genuine customer complaints can also occur.
However, identifying unusual patterns can help businesses distinguish between normal customer feedback and possible coordinated activity.
Fraud prevention often requires data from multiple online sources.
Get Data For Me can help businesses collect and organize publicly available data based on their specific business requirements.
This can support use cases such as:
By collecting data in a structured format, businesses can spend less time on manual research and focus more on analyzing potential risks.
Get Data For Me can help businesses build data collection workflows designed around their specific requirements.
Every e-commerce business faces different risks.
A marketplace seller may need to monitor unauthorized resellers, while an online retailer may need to track unusual product listings or pricing patterns.
A custom data collection solution can be designed around the specific information a business needs.
Depending on the project, this may include:
This targeted approach helps businesses collect relevant data instead of processing large amounts of unrelated information.
Fraud prevention data collection should be performed responsibly.
Businesses should consider:
Web scraping should not be treated as a way to collect sensitive personal or financial information without proper authorization.
A responsible data collection strategy helps businesses use web data while reducing unnecessary legal and privacy risks.
Web scraping can provide valuable information, but it works best when combined with other fraud prevention tools and processes.
Do not make decisions based on a single signal.
Instead, combine information such as:
Multiple signals can provide a more reliable risk assessment.
Fraud patterns change over time.
Regular data collection can help businesses identify new threats and monitor changes in online activity.
Collected data should be checked for:
Poor-quality data can lead to inaccurate conclusions.
Not every unusual activity is fraudulent.
For example:
Fraud prevention systems should use risk-based decisions and additional verification where appropriate.
Before collecting data, businesses should review relevant legal requirements and website policies.
Responsible data collection helps protect both the business and its customers.
E-commerce fraud continues to evolve as online businesses grow. Fraudsters use stolen payment information, fake accounts, automated tools, marketplace listings, and other methods to exploit online businesses.
Web scraping can support fraud prevention by helping businesses collect relevant public data from multiple online sources.
Businesses can use this information to monitor marketplace activity, analyze price patterns, identify suspicious sellers, track public fraud intelligence, and understand emerging threats.
However, web scraping should not be viewed as a complete fraud prevention system. It works best as part of a broader strategy that combines internal transaction data, risk analysis, verification systems, and responsible data collection.
With the right data collection strategy, businesses can gain better visibility into online activity and make more informed decisions about potential fraud risks.
Yes. Web scraping can support fraud prevention by collecting relevant publicly available data from marketplaces, websites, public reports, and other online sources. This data can help businesses identify suspicious patterns and emerging threats.
Depending on the use case, businesses may collect publicly available information such as product listings, seller information, prices, reviews, public fraud reports, and other relevant online data.
Web scraping can support fraud intelligence by collecting information from legitimate and authorized public sources related to reported fraud indicators. Businesses should not unlawfully collect or process sensitive financial information.
Yes. Review data can be analyzed for patterns such as repeated language, unusual timing, and coordinated activity. These patterns may help identify potentially suspicious review activity.
Yes. Businesses can monitor public marketplace listings, seller profiles, product prices, and other publicly available information to identify unusual seller activity.
The legality of web scraping depends on factors such as the type of data collected, the source, applicable laws, website terms, and how the data is used. Businesses should review relevant legal and privacy requirements before collecting data.
No. Web scraping can provide additional data and intelligence, but it should generally be used alongside internal transaction monitoring, fraud detection systems, identity verification, and other security measures.