AI agents: Powering a new wave of fraud disrupting market research programs

Research teams are embracing AI tools at a record pace. 89% of researchers are already using AI for transcription, data cleaning, and analysis. But the same technology creating efficiencies for researchers is also powering a new category of participant fraud that's harder to detect than anything the industry has seen before.

AI-powered fraud goes beyond the bot problem research teams have managed for years. Today's scammers use sophisticated AI agents that understand survey logic, craft thoughtful responses, and mimic human behavior patterns in ways that slip past traditional detection methods.

New research from Tremendous and Escalent reveals how AI-generated responses are affecting data quality — the foundation of market research. We spoke to research leaders across the industry about the fraud they're experiencing and the strategies that are actually working to protect their studies in 2025 and beyond.

Quick stats: AI Fraud in research in 2025

• 30-40% of survey data now contains questionable responses (up from 10-15%)

• 1 in 3 fraudsters uses ChatGPT for open-ended responses

• 69% of data quality flags linked to fraud

• 80% of clients prioritize data quality when selecting research partners

What are AI agents in research fraud?

Definition: AI agents are programs that use artificial intelligence to complete surveys at scale, generating legitimate, human-like responses to collect research incentives. They differ from simple bots because they can adapt to survey logic, vary their writing style, and produce answers that appear genuinely human — easily bypassing traditional bot detection methods.

"We used to see 10 to 15% questionable data, but now it's closer to 30 to 40%. The problem has grown significantly," says Mary Draper, VP of Business Development at EMI Research Solutions.

Here's what makes AI agents particularly challenging: they can understand context, express uncertainty when appropriate, and create responses that are longer and more detailed than typical bot answers. One in three fraudsters now uses AI tools like ChatGPT for open-ended responses, creating answers that sound polished but lack genuine emotion or insight.

The economics driving this trend are straightforward.

When fraudulent responses mix with legitimate data, teams face budget concerns, decreased data quality, and resource-intensive data cleanup tasks — often after incentives have already been paid out.

How to detect AI fraud in market research data

Research teams are discovering that existing fraud detection tools can’t keep up with AI-generated responses that mimic human writing. The challenge isn't just technical — it's also operational, financial, and strategic.

What makes AI fraud different from traditional bots?

Today's AI fraud operates on three levels, each presenting unique detection challenges:

• AI-powered bots use machine learning to closely mimic human behavior in real-time, generating realistic open-ended responses and adapting to survey logic. These aren't the simple, repetitive bots researchers have dealt with before.

• Humans using AI tools represent an even bigger challenge. "The bigger issue isn't just traditional bots and scripts: it's humans using AI in clever ways to bypass detection. That's much harder to catch," says Nate Lynch, Owner and Co-CEO of Full Circle Research Company.

• Fraudsters with sophisticated setups use proxies and anti-detect browsers to create multiple fake participants from one location, making detection nearly impossible with traditional methods.

The limitations of current fraud detection methods

Standard fraud detection methods are falling short. Here's what research teams are finding:

• Behavioral monitoring tracks typing speed and mouse movement, but advanced AI can now mimic these patterns. Tools like device fingerprinting help, but fraudsters can bypass them with anti-detect browsers.

• Linguistic analysis catches overly formal or formulaic text, but AI-generated responses are becoming increasingly natural-sounding. Even specialized tools like OpinionRoute and ReDem can struggle with these types of responses.

• Hidden validation questions and honeypot traps catch basic bots, but advanced AI systems can process images and detect hidden prompts.

Today, 69% of all data quality flags in surveys are linked to various forms of fraud, with existing tools catching only a fraction of sophisticated AI responses. Researchers need to evolve their fraud detection and data cleaning approaches to stay one step ahead.

What AI responses are missing

While AI is getting better at mimicking human language, it still can't replicate the nuanced emotions and contextual behaviors that shape real decisions. AI responses usually lack genuine emotional depth and can be inconsistent from answer to answer, making them unreliable for high-stakes business decisions like product launches and pricing strategies.

As Amanda Keller-Grill from InnovateMR notes: "The gray area is around respondents: is it okay if someone uses ChatGPT to write their open-ended response? Most would say it's not acceptable because it removes the authentic human perspective that research is designed to capture."

The new fraud prevention playbook: Research defense in the AI era

Download the full report

Impact of AI fraud on market research results

The financial and operational consequences of AI fraud go beyond derailing individual studies. Research teams are dealing with immediate budget concerns and longer-term strategic challenges that affect how they operate.

Resource drain on research teams

John LaFrance, Vice President of Research Methods and Sampling Operations at Escalent, puts the scale in perspective: "We're paying out millions in incentives to fraudsters. Without proper detection tools, researchers may unknowingly reward bad data and waste valuable time and resources."

The cost breakdown looks like this:

• Direct financial losses: Incentive payments go to fraudsters rather than legitimate participants, with no way to recover funds once payouts are processed.

• Labor-intensive cleanup: Teams spend hours validating responses that initially appear legitimate, only to later find issues that force them to restart data collection entirely.

• Increased participant costs: Verified human respondents command higher fees as authentic data becomes more expensive to source reliably.

• Project delays: Re-running studies due to fraud detection extends timelines and increases overall research costs.

Business risks from unreliable data

When AI-generated responses slip through quality checks, they create significant risks for organizations relying on research insights, including:

Distorted market signals: Fake responses muddy actual consumer behavior patterns, making it harder to identify genuine trends and preferences.

Strategic missteps: Product launches, pricing decisions, and brand positioning based on fraudulent data can lead to missed opportunities and less optimal business outcomes.

Stakeholder confidence erosion: When research findings don't match market reality, internal teams and external clients lose trust in research capabilities.

The stakes are particularly high for research firms.

How AI fraud is reshaping the research industry in 2025

Researchers aren't waiting for perfect solutions to emerge. They're adapting their methodologies and operational approaches to address participant fraud while maintaining data quality standards.

There are three key changes reshaping how research gets done in 2025 and beyond:

1. Blended methodologies are in demand: Clients increasingly request mixed qual-and-quant approaches that combine quantitative scale with qualitative validation. These hybrid studies make it harder for AI agents to fake convincing responses throughout the entire research process.

2. Traditional methods are making a comeback: "More clients are revisiting traditional research methods, like phone surveys, text-based outreach, and in-person or virtual intercepts," says Mary Draper from EMI Research Solutions. "In over a decade in online research, I haven't seen this level of interest in mixed modes until now." While these methods are more expensive and time-consuming than online surveys, they make it harder for AI-powered inputs to pass validation. 

3. Premium pricing for verified participants is on the rise: Teams are implementing tiered service offerings with varying levels of fraud protection. Higher-quality verification comes at increased cost, but clients are now willing to pay for guaranteed authentic insights on high-stakes projects.

Industry-wide changes on the horizon

The research industry is preparing for significant structural shifts as AI fraud concerns drive new standards and collaborative approaches.

Cross-industry collaboration is accelerating: Organizations like the Data Quality Co-Op, AAPOR, and the Market Research Society are facilitating knowledge sharing about fraud prevention tactics. Teams are finding that collective defense strategies strengthen everyone's capabilities.

Government regulation is anticipated: As Christopher Barnes, President of Escalent, notes, "AI regulation is inevitable. Governments will need to step in soon." Leading research teams are documenting their processes and maintaining audit trails in preparation for more regulated requirements.

Technology partnerships are expanding: Researchers are integrating specialized fraud detection platforms like Verisoul, Research Defender, and Roundtable alongside traditional survey tools. Many are also using incentive platforms like Tremendous to provide a final safeguard by flagging suspicious redemptions based on IP address, country, email address (ex. detecting multiple email addresses tied to the same device or account), and other network signals.

Key takeaways: The new reality of the research industry

The research industry is at an inflection point where traditional approaches to data collection and validation are evolving rapidly. 

• Sophisticated fraud detection is becoming critical. Current detection methods catch only a fraction of AI responses, increasing the risk of unreliable data in studies across the research ecosystem.

• The cost of quality data is rising. Verified human participants require a higher investment in screening, verification, and incentives. Teams that budget for enhanced fraud prevention now will be better positioned than those that wait.

• Human insights are more valuable than ever. As AI adoption continues to accelerate, authentic human responses become the premium differentiator to drive strategic business decisions. Research teams that can guarantee human authenticity will command both higher prices and stronger client loyalty.

• Collaborative defense is essential. No single solution can fight fraud alone. Teams are assembling toolkits of complementary solutions for behavioral monitoring, linguistic analysis, device fingerprinting, and payout fraud detection to protect their studies. Researchers are also sharing knowledge through industry forums and initiatives like Data Quality Co-Op and AAPOR to strengthen everyone's collective defenses.

For comprehensive strategies to protect your research programs from AI fraud, including the latest detection methods, industry best practices, and additional expert insights, read the full report from Tremendous and Escalent.

How Tremendous helps protect research incentive programs

Tremendous makes it easy to send research incentives to participants globally — fast and for free.

Our fraud prevention tools use data across millions of payouts to flag suspicious redemptions before money goes out the door, saving researchers thousands of dollars each year. Get accurate protection for your incentives program with customizable fraud controls based on country, IP address, dollar amount redeemed, and more.