Fraud Prevention in E-commerce with Web Scraping

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.

What Is E-commerce Fraud?

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:

• Stolen payment card fraud
• Account takeovers
• Identity theft
• Friendly fraud
• Refund fraud
• Promotion abuse
• Fake accounts
• Reshipping scams
• Triangulation fraud

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.

Common Types of E-commerce Fraud

Stolen Payment Card Fraud

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

Account takeover fraud occurs when criminals gain unauthorized access to a customer’s account.

They may use stolen login credentials to:

• Make purchases
• Change account information
• Access stored payment details
• Use loyalty points
• Exploit customer rewards

Monitoring unusual login and transaction behavior can help businesses identify suspicious account activity.

Friendly Fraud

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

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

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.

How Does Web Scraping Support Fraud Prevention?

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:

• Public fraud reports
• Online marketplaces
• Public forums
• Review platforms
• Publicly available threat intelligence
• Public seller information
• Product listings
• Price changes

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.

Gather External Intelligence

Fraudsters often leave digital traces across different websites and platforms.

For example, suspicious activity may appear as:

• Repeated listings from new seller accounts
• Unusual pricing patterns
• Duplicate product listings
• Suspicious review activity
• Public reports about fraudulent accounts

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.

Analyze Data from Multiple Sources

Fraud detection becomes more effective when businesses can compare information from different sources.

For example:

Internal Transaction Data
+
Marketplace Data
+
Public Fraud Reports
+
Seller Information
=
Broader Risk Analysis

A single data point may not indicate fraud. However, multiple suspicious signals appearing together may justify additional investigation.

Support Faster Risk Monitoring

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.

How Web Scraping Can Help Identify Stolen Payment Card 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:

• Public fraud databases
• Security reports
• Public threat intelligence
• Fraud warnings
• Publicly reported compromised information

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.

Monitor Public Fraud Intelligence

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:

• A new type of payment scam
• A recurring fraud pattern
• A new account takeover technique
• A coordinated attack targeting online retailers

This information can help security and fraud teams update their detection rules.

Monitor Public Fraud Discussions

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.

Analyze Transaction Velocity

Transaction velocity refers to the frequency and speed of transactions associated with an account, payment method, device, or other identifier.

Unusual activity may include:

• Multiple purchases within a very short period
• Several failed payment attempts
• Many accounts using similar information
• Repeated orders from unusual locations

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.

How Can Web Scraping Help Identify Suspicious IP Activity?

IP intelligence can provide additional context when analyzing online activity.

Businesses may use IP-related data to identify:

• High-risk network activity
• Known proxy services
• VPN connections
• Suspicious hosting providers
• Geographic inconsistencies

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.

Compare Data Against Public Risk Lists

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.

Identify VPNs and Proxies

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:

• Unusual order behavior
• Multiple failed payments
• Account age
• Shipping information
• Transaction velocity

Using multiple signals provides a more accurate assessment than automatically blocking every VPN user.

Perform Geolocation Checks

Businesses may compare the approximate location of a connection with other transaction details.

For example, an order may require additional review when:

• The IP location is significantly different from the shipping location
• A customer’s activity suddenly moves between distant regions
• Multiple accounts appear to originate from the same unusual location

Geolocation should be treated as a supporting signal rather than conclusive proof of fraud.

How to Monitor Price Scams and Arbitrage

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:

• Product listings
• Seller information
• Prices
• Listing frequency
• Product availability
• Marketplace activity

This can help identify unusual activity involving a company’s products.

Track Price Discrepancies

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.

Identify Unusual Bulk Purchases

Fraudsters may attempt to purchase large quantities of high-demand products.

Monitoring unusual purchasing patterns can help businesses identify:

• Sudden order volume increases
• Repeated high-volume purchases
• Multiple accounts ordering similar products
• Large purchases from newly created accounts

Businesses can then apply additional verification or review processes when appropriate.

How Can Customer Feedback Data Help Detect Suspicious Activity?

Customer feedback can provide useful information about unusual activity.

Businesses can analyze reviews, ratings, and public discussions to identify patterns that may indicate:

• Fake accounts
• Coordinated review activity
• Review manipulation
• Reputation attacks
• Suspicious customer behavior

Web scraping can help collect publicly available feedback from multiple platforms for analysis.

Detect Fake Reviews

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.

Identify Review Bombing

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
↓
Sudden Increase in Negative Reviews
↓
Multiple Accounts Posting Similar Feedback
↓
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.

How Get Data For Me Helps with E-commerce Fraud Prevention

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:

• Marketplace monitoring
• Seller monitoring
• Price monitoring
• Public fraud intelligence collection
• Review analysis
• Competitor and product monitoring

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.

Custom Data Solutions

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:

• Specific websites
• Specific marketplaces
• Product categories
• Seller profiles
• Public review platforms
• Geographic regions
• Pricing information

This targeted approach helps businesses collect relevant data instead of processing large amounts of unrelated information.

Data Compliance and Responsible Collection

Fraud prevention data collection should be performed responsibly.

Businesses should consider:

• Applicable privacy laws
• Website terms and conditions
• Data protection requirements
• The nature of the information being collected
• Whether the data is publicly available
• How the collected data will be stored and used

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.

Best Practices for Using Web Scraping in Fraud Prevention

Web scraping can provide valuable information, but it works best when combined with other fraud prevention tools and processes.

Use Multiple Risk Signals

Do not make decisions based on a single signal.

Instead, combine information such as:

• Transaction behavior
• Account activity
• IP intelligence
• Product activity
• Marketplace data
• Public fraud reports

Multiple signals can provide a more reliable risk assessment.

Keep Data Fresh

Fraud patterns change over time.

Regular data collection can help businesses identify new threats and monitor changes in online activity.

Validate Collected Data

Collected data should be checked for:

• Duplicate records
• Missing values
• Outdated information
• Incorrect URLs
• Data quality issues

Poor-quality data can lead to inaccurate conclusions.

Avoid Automatically Blocking Legitimate Customers

Not every unusual activity is fraudulent.

For example:

• A customer may use a VPN
• A customer may travel internationally
• A customer may purchase multiple products legitimately
• A business may place a large order

Fraud prevention systems should use risk-based decisions and additional verification where appropriate.

Follow Responsible Data Collection Practices

Before collecting data, businesses should review relevant legal requirements and website policies.

Responsible data collection helps protect both the business and its customers.

Conclusion

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.

Frequently Asked Questions

Can web scraping help prevent e-commerce fraud?

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.

What data can be collected for e-commerce fraud prevention?

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.

Can web scraping identify stolen payment cards?

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.

Can web scraping detect fake reviews?

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.

Can web scraping monitor fraudulent sellers?

Yes. Businesses can monitor public marketplace listings, seller profiles, product prices, and other publicly available information to identify unusual seller activity.

Is web scraping legal for fraud prevention?

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.

Does web scraping replace fraud detection software?

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.