Topic starter
11/07/2026 8:36 pm
TL;DR: Identity verification can improve conversion only when it also changes how risky users are separated from trusted ones, according to Prove Identity research. Leading firms in its case study reported up to 78.7% shorter onboarding time, a 28.8% increase in approved applications, and a 151% rise in new patient registrations, while also shrinking fraud into a 7% self-selection bucket that is easier to inspect.
Editorial analysis by NHI Mgmt Group, based on content published by Prove Identity: “The Unexpected Way That Leading Firms Are Reducing Fraud by 75% While Accelerating Onboarding by 79%”.
By the numbers:
- Company A reported a 78.7% decrease in average onboarding completion time, from 47 seconds to 10 seconds.
- Company A also reported a 28.8% increase in total applications approved, from 73% to 94%.
- Company C experienced a 151% increase in new patient registrations after Prove Pre-Fill was implemented.
Key questions
Q: What breaks when identity verification is too slow or fragmented across onboarding steps?
A: When verification is slow or fragmented, applicants abandon the process, staff create manual workarounds, and risky cases can slip through inconsistent review paths.
Q: Why do fast onboarding and low-friction payment flows create more fraud risk?
A: Fast onboarding and low-friction payment flows create fraud risk because they reduce the signals teams can use to distinguish a trusted customer from an organised fraudster.
Q: How do teams know if identity security controls are actually working?
A: Identity security controls are working when teams can show a current view of high-risk entitlements, detect privilege drift quickly, and remove access before exposure spreads.
Practitioner guidance
- Map onboarding friction to risk tiers Separate low-risk applicants from higher-risk applicants before manual review so the default path stays fast without removing scrutiny from edge cases.
- Treat opt-out behaviour as a risk signal Flag users who refuse auto-fill or abandon identity automation for investigation, because that behaviour can indicate a mismatch between claimed and actual identity.
- Measure conversion and assurance together Track completion time, approval rate, and fraud review yield in one dashboard so fraud controls are judged by both trust and journey outcomes.
Bottom line: Identity verification can improve both fraud control and onboarding efficiency when it separates routine users from higher-risk applicants.
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11/07/2026 9:05 pm
Selective friction is the real control here: the article shows that identity verification is most effective when it routes trustworthy users quickly and forces suspicious applicants into a narrower review path. That changes the economics of onboarding because the system no longer treats every applicant as equally trustworthy. For practitioners, the question is whether identity controls separate risk, not merely whether they collect data.
A few things that frame the scale:
- Nearly 60% of companies reported that fraud losses were still increasing in 2025.
A question worth separating out:
Q: When should teams prioritise fraud review over pure onboarding speed?
A: Prioritise fraud review when the identity flow shows signs of mismatch, manual entry, or repeated refusal of automation. Those are the points where risk is being expressed, and they are more valuable than adding friction across the entire population. Good governance focuses review effort where behaviour suggests the claim and the claimant may not align.
👉 Read our full editorial: Identity verification can cut fraud and onboarding friction