Great research starts before the first question is asked. If a participant shouldn't be in your study, the best time to catch that is before they enter, not after your team has spent hours analysing compromised data.
That principle shapes everything about how Askable approaches panel quality. We don't bolt on checks at the end. We design quality into the participant lifecycle from the ground up; layering prevention, detection, and continuous improvement so that each measure reinforces the others.
Our Philosophy: Quality Belongs Upstream
Most research platforms treat participant quality as a filtering problem: run the study, then sort the good data from the bad. The issue is that by then, the damage is done. Researcher time has been spent. Incentives have been paid. Timelines have slipped. And confidence in the data is already compromised.
Askable's approach is fundamentally different. We treat quality as a design problem; something that should be solved at each stage of the participant lifecycle, not retrospectively. Every layer of our quality system exists because we asked: "What's the earliest point we can catch this?"
Layer 1: Identity Verification — Raising the Cost of Fraud
Fraudulent participants rely on one thing: being able to create accounts cheaply and at scale. Our identity verification is designed to make that economically unviable.
PayPal identity verification — every participant must link a verified PayPal account. This isn't just an identity check — it ties participation to a financial record that's difficult and costly to fabricate at scale. A scammer can create a dozen email addresses in minutes; creating a dozen verified PayPal accounts is a fundamentally different proposition.
Phone re-verification — all participants complete phone-based verification. Like PayPal, this adds a real-world identity anchor that's resistant to mass duplication.
CAPTCHA at onboarding — automated checks block scripted sign-ups. This is the baseline, not the ceiling — it catches unsophisticated bots so the more advanced systems can focus on subtler threats.
The principle here is layered friction: each check is individually simple for a legitimate participant, but the combination makes large-scale fraud operationally expensive.
Layer 2: Fraud Detection — Catching What Verification Misses
Identity checks prevent the obvious cases. But sophisticated actors — VPN users, professional survey takers, people operating multiple accounts — can pass static verification. That's why our detection systems are behavioural, not just credential-based.
VPN detection & blocking — participants using VPNs or proxy services are flagged and blocked. This matters because location masking is one of the most common methods used to circumvent geographic targeting in screeners. If a study requires participants in Sydney, we ensure they're actually in Sydney.
Behavioural bot detection — we deliberately chose behavioural analysis over CAPTCHA-only approaches. CAPTCHAs are increasingly solvable by automated tools. Behavioural analysis detects non-human interaction patterns that static challenges miss — and it evolves as the threats do.
IP monitoring — daily monitoring detects participants operating multiple accounts from the same IP address. This catches a pattern that no single-session check can: the same person appearing as different participants across studies.
Country-level blocking — known high-fraud regions are restricted at the network level. This is a pragmatic measure based on pattern analysis of where organised participant fraud originates.
The design intent across this layer is defence in depth: no single check catches everything, so we layer complementary detection methods that cover each other's blind spots.
Layer 3: AI-Powered Quality Screening — Systemic, Not Per-Study
Individual screeners can be gamed. A savvy participant can learn what answers a study is looking for. But inconsistency across studies is much harder to fake — and that's exactly what our AI systems are designed to detect.
AI screener analysis — our system analyses screener responses across a participant's entire history on the platform. If someone claims to be a marketing manager in one study and a software engineer in another, that inconsistency is flagged. This is a fundamentally different approach to per-study screening: it treats quality as a longitudinal signal, not a point-in-time check.
AI-moderated onboarding assessment (rolling out) — new participants complete a short AI-moderated conversation during sign-up. The transcript is analysed for response quality, thoughtfulness, and consistency. This is intentionally designed to mirror real research conditions — the quality check is the methodology. If a participant can't engage meaningfully in a 5-minute AI conversation, they're unlikely to deliver value in a 60-minute interview. Participants who repeatedly fail are automatically removed.
Layer 4: Ongoing Monitoring — Quality as a Continuous Programme
Panel quality isn't a problem you solve once. Fraud methods evolve, participant circumstances change, and quality signals accumulate over time. Our monitoring is designed to be continuous, not periodic.
Dedicated participant quality team — real people reviewing flagged accounts, investigating reports, and making judgment calls that automated systems can't. Automation handles scale; human oversight handles nuance.
Daily IP audits via real-time monitoring dashboards — not weekly reports, not monthly reviews. Daily. Because the faster you catch a bad actor, the less damage they do across the platform.
Participant blacklisting — reported or confirmed bad actors are permanently removed. This protects not just the researcher who flagged the issue, but every future study on the platform.
Participant exclusion controls — researchers can exclude specific participants from their studies, and our team applies quality-based filters from previous sessions. Your quality standards compound over time.
Compliance & Certifications
Our commitment to data integrity extends beyond participant quality into our security and privacy infrastructure:
Certification | Scope |
ISO 27001:2022 | Information Security Management |
ISO 27701:2019 | Privacy Information Management |
SOC 2 Type 1 & 2 | Security, Availability, Confidentiality |
ISO 42001 | AI Governance (passed audit January 2026) |
UK Cyber Essentials | Baseline Cyber Security |
GDPR Compliant | Data Protection |
Full details at trust.askable.com.
By the Numbers
Metric | Value |
Verified participants in our panel | 1,000,000+ |
Average participant show rate | 97.8% |
AI-moderated interviews completed | 38,000+ |
Countries covered | 48+ |
Cities represented | 5,300+ |
Why This Matters for Enterprise Research
Enterprise research teams don't just need quality participants, they need confidence in the system that delivers them. When you're running research across teams, markets, and time zones, you need to know the methodology is sound, not just the metrics.
Askable's approach is designed around a simple conviction: quality is a design problem, not a filtering problem. Every layer exists for a reason. Every measure reinforces the others. And the system gets smarter over time, not just bigger.
For more detail on any of these measures, or to discuss how Askable's panel quality programme fits your specific requirements, reach out to your Askable contact or email support@askable.com.