The Pervasive Threat of Tech-Enabled Fraud in Survey Research

It’s easy to spot fraud in your survey data, right? No, not really. We are in a new era of tech-enabled fraud, where fraudsters no longer appear as obvious bots or provide patently bad, open-ended responses, but instead mimic clean, usable responses. The evolution of fraud has quietly outpaced many of our traditional defenses, permeating proprietary panels and sampling sources. Without a more proactive, technology-driven approach, even sophisticated researchers may draw conclusions from compromised results.

Tech-Enabled Fraud Is Pervasive across Sample Sources

In our recent research-on-research study, conducted in March and April across six of the leading online sample sources, we flagged more than 30% of respondents as suspicious or outright fraudulent. These weren’t fringe cases or isolated incidents. Every sample source we tested, proprietary panels and large exchanges alike, showed similar rates of tech-enabled fraud and had hyperactive survey-takers present in their respondents.

What’s especially concerning is how this fraud presents itself. Traditional detection methods, such as post-survey cleaning or logic traps, are increasingly ineffective against today’s fraud tactics. Sophisticated actors use IP-masking, device spoofing, cookie clearing, browser emulators, and AI-generated text to convincingly impersonate legitimate respondents. Once these bad actors enter a survey (and enter repeatedly), they camouflage themselves very well, avoiding the usual quality triggers like attention checks and open-ended response checks.

Fraud Doesn’t Look Extreme; It Looks Average

Many researchers assume that fraudulent data will skew results with exaggerated or illogical answers. But our study found something more subtle, and arguably more damaging – fraudsters often provide flattened, undifferentiated responses.

Take brand awareness, for example. In aided awareness exercises across hotel and streaming brands, flagged respondents consistently selected fewer brands. Not only that, their selections showed less distinction between high-awareness and low-awareness brands. Among qualified respondents, leading hotel brand Marriott enjoyed 71% aided awareness. Among fraudsters, that dropped by 12 points. The same pattern held for other top brands (e.g., Hilton, Best Western, Hyatt) — consistent attenuation of results, dragging down top brands while not penalizing low-awareness brands, thus creating artificially small differences across brands.

This effect wasn’t limited to brand metrics. On benchmark questions like self-reported health and holding a valid U.S. passport, where national statistics provide a reliable baseline, fraudulent respondents consistently overreported. They were more likely to say their health is excellent, more likely to say they hold a valid passport, and even more likely to report smoking daily, despite claiming superior health.

These inconsistencies may not trip a data review, but they distort the truth. They inject noise and bias into results that can meaningfully shift KPIs.

Repeat Offenders, Masked Identities

A major driver of this new wave of fraud is repeat participation. Using proxies, VPNs, and other identity-masking tools, professional survey takers can enter the same survey dozens (or hundreds) of times.

In our study, we identified respondents who had attempted more than 1,000 surveys in a 24-hour period. That isn’t accidental on respondents’ part, but a coordinated effort to abuse systems and harvest incentives at scale. Hyperactive respondents were present in every panel and exchange, despite varying degrees of vetting and access control.

One might assume proprietary panels fare better. They didn’t. The overall fraud rates between panels and exchanges were statistically similar. In some cases, panels had higher concentrations of hyperactive behavior. Panel A, for instance, showed nearly 25% of its traffic came from users flagged for excessive activity, more than one in four. While other panels fared better, we saw respondents in every panel attempting hundreds of surveys in the previous 24-hour period.

Taking Action to Ensure Higher Data Quality

Cleaning up fraud after the fact is insufficient. By the time a fraudulent respondent is flagged post-survey, quotas may be filled, incentives paid, and decisions already made. Worse still, these respondents may never be flagged for removal, given how ordinary they often appear.

That’s why the industry needs to shift from reactive cleaning to proactive prevention. At Rep Data, we use a layered approach that screens every respondent before they enter a survey. Our Research Defender platform uses enhanced digital fingerprinting, AI-driven open-end analysis, and behavioral risk scoring to block bad actors in real time.

This system doesn’t just look at what a respondent says; it evaluates how they behave. Are they copying and pasting? Are they spoofing their device? Are they typing with human cadence or with script-level speed? These behavioral signals offer early warnings that content analysis alone can’t provide.

The Path Forward

The findings from our research are clear: fraud is systemic, subtle, and surprisingly consistent across sources. No provider is immune. Panels and exchanges alike show similar vulnerabilities, and even well-known sources require independent verification.

That’s why researchers must take ownership of quality at every stage of the process. It’s not enough to rely on reputational trust or assume fraud has been handled elsewhere. Data quality demands active monitoring, shared accountability, and an honest look at who your respondents are and how they are behaving, not just what they say.