FRAUD PREVENTION TECHNOLOGY

Errors occur in spite of careful study design, conduct, and implementation of error-prevention strategies. Research For Good’s best practices, outlined below, identify, correct and minimize any errors or impact on study results.

Digital Fingerprinting

Digital Fingerprinting enables us to create a unique identity for each computer. Using this method, no single computer (regardless of how many people may share that computer) is allowed to enter any single survey more than once. The Digital Fingerprint itself is a proprietary combination of many characteristics of the user’s browser and computer. This creates a statistically large basis set of fingerprints, sufficiently larger than any single survey would ever require. Digital Fingerprinting effectively eliminates the possibility of a survey being taken twice on the same computer, even if its IP address changes and even if the cookies/cache have been cleared.

SecureGeoIP (TM)

Our proprietary SecureGeoIP (TM) technology is based on a proprietary algorithm that uses IP Geo-location and known routing latencies to evaluate the truthfulness of a respondent’s declared postal code. It effectively cross-references IP & Postal code distances from one another. Thorough and in-depth analysis has indicated that this measure is an excellent predictor of fraud, while still allowing legitimate, quality respondents through to client surveys. The addition of SecureGeoIPTM to our arsenal of quality control measures, therefore, results in a higher level of data quality without sacrificing response and conversion rates through false positives.

CAPTCHA

I am not a robot. Sounds simple enough, but as we know, it can be hard to detect ever more sophisticated bots attempting to defraud surveys. To combat this, we’ve implemented the most sophisticated anti bot security measure available, CAPTCHA.

Active Detection of Connections from Proxy Servers

While the known proxy server list is large and always being updated, new proxy servers are always being turned on as fast as proxy server lists can be updated. Fraudulent entities may also be running their own proxy servers or using VPNs to hide their identity and location. In addition to the passive IP blocking of known proxy servers mentioned above, RFG has developed our own proprietary active proxy detection. The IP of every respondent is analyzed and scanned for the presence of a proxy server. Any respondents using proxy servers are blocked from entering surveys.

IP Filtering

No respondent utilizing the same IP address is allowed to re-enter a single survey.

IP Authentication Through Known Suspect Online Activity Database

Research For Good authenticates all respondents through a database of known suspect online activity IP addresses and blocks those who do not meet our stringent threshold of quality. This allows us to prevent users with a history of suspicious activity from participating in our surveys, and we can take advantage of shared history to prevent these people from entering our client’s survey the very first time we see them, and not having to wait for undesirable behavior within our own system.

GEO-IP Validation

For geo-targeted studies, we check the respondent’s geo- IP to confirm that it is within the acceptable range of the study’s targeted location.

IP Blocking of Known Proxy Servers

It is well established that those who are intentionally trying to take surveys for illicit profit are hiding behind anonymous proxy servers in order to mask their identity, frequently change IP address, and also hide their true geo-IP location. Research For Good has built a web crawler that scrapes the IP addresses of known anonymous proxy servers from many websites. This crawler re-checks the sites multiple times per day and any respondents using these proxy servers are blocked from entering client surveys. By blocking such respondents, RFG is eliminating a predominant source of fraud.

Suggested Additional Data Quality Measures

In addition to the measures Research For Good has implemented, we also suggest that clients continue to take their own steps to further improve survey data quality. Some strategies may include:

  • Actively running your own digital fingerprinting technology on all surveys
  • Any surveys showing respondents proprietary and confidential information (movie trailers, images, product feature descriptions) should include client’s Privacy and Terms & Conditions verbiage within the survey itself and require respondents to agree.

Research For Good continues to proactively enhance our security measures to consistently offer the highest quality online sample possible.

For more information, please contact us.