Which identity verification vendors use AI to improve pass rates?

"AI improves pass rates" is one of those claims that's easy to say and hard to prove. What does it actually mean for the person trying to sign up, and for your ROI as a company?

In practice, AI can influence a pass rate at several different points in the journey: it can flag exactly where genuine users are dropping out, recommend specific changes to fix it, guide someone through a better document photo in the moment, match a selfie to an ID, and offer a fallback path when the first verification attempt comes up short. 

In this article, we will delve into how AI can improve pass rates on a practical level, and also introduce you to our identity verification solutions that use AI to improve pass rates.

What "improving pass rates" should actually mean

First things first: it shouldn't mean lowering the bar so more applicants slip through. As you probably already know, approving unverified users just to inflate a completion number is a fast way to create financial and regulatory exposure.

Real improvement means verifying more genuine customers without loosening necessary fraud controls. When a verification flow gets better at separating honest applicants from bad actors, conversion goes up without opening the door to synthetic identity fraud and more.

How GBG Foresight improves pass rates across the verification journey

Applying AI across an entire onboarding flow – rather than to one isolated check – is what helps you find issues that a single static rule would struggle to catch and, as a result, helps your team act in real time. Our AI-powered Foresight solution tackles this in four ways:

Finding where the problem is

Before anything can be fixed, someone has to know exactly where genuine users are dropping out. Our Foresight solution tracks that continuously.

Our solution:

• Tracks pass rates alongside completion volumes: Monitoring conversion trends together with overall traffic identifies sudden performance shifts before they affect monthly acquisition goals.
• Breaks down outcomes by customer cohort: Segmenting data by age, region, or device type reveals specific points of friction that overall averages often don’t show.
• Connects performance with configuration edits: Mapping conversion changes against recent workflow updates shows whether a drop coincides with a new verification step.
• Sets automated anomaly alerts: Automated notifications flag unexpected volume spikes or sudden pass-rate drops, allowing you to investigate immediately.

Determining how much room you have to improve

A low conversion rate in a specific segment doesn't always mean your process is broken. Some markets or demographic groups naturally carry higher inherent risk or have thinner public records.

Our Foresight solution provides external context by comparing your internal outcomes against anonymised performance data from similar businesses. We draw on anonymised outcomes from more than 20,000 customers across 195 countries, and every completed check adds another data point to that foundation. As Gus Tomlinson, Chief Product and Technology Officer at GBG, puts it:

"Every identity check we run makes the next one more accurate. That history is our asset, and the outcomes it drives are what our customers care about. Until now, the intelligence sitting inside that data has not been accessible in a way that lets teams act on it directly. Foresight changes that."

Recommending what to change

Knowing that a performance gap exists is only the first step. Your next challenge is identifying why the gap formed and deciding what changes your team should make.

Rather than forcing you to interpret raw analytics on your own, our Foresight product uses machine learning models to convert performance trends into specific adjustments:

• Analyses metrics alongside peer benchmarks: Combining internal conversion data with market benchmarks highlights specific areas where your workflow underperforms.
• Suggests actionable workflow edits: The system recommends concrete adjustments to rules, data sources, or capture methods rather than simply reporting low numbers.
• Provides clear reasoning for every suggestion: Explaining the underlying data behind each recommendation gives you full visibility into why a change is suggested.
• Tests adjustments in real time: You can run controlled tests on recommended changes to measure the impact on conversion before full deployment.

For example, a business started with a 70% completion rate in a key market. Our Foresight solution analysed peer data from similar organisations and identified that an 80% pass rate was realistic for that specific cohort. 

Our system recommended adding a UK credit reference agency check to capture thin-file users. After testing the change, real-time performance tracking confirmed a 10% increase, bringing the overall pass rate to 80%.

Giving genuine customers multiple ways to verify successfully

The first three layers are about diagnosing and fixing pass-rate problems at the system level. But we also focus on the individual applicant in the moment, making sure a first verification attempt that comes up short doesn't end the journey.

Starting with authoritative data sources across global markets maximises initial match rates, especially if you use our Multi-Source solution.

When database records can't verify a user confidently, secondary methods like document verification and biometric facial recognition gather additional evidence instead of rejecting the applicant outright.

Here’s an example of how the automated AI response could react:

Four questions to ask when an identity verification vendor claims AI improves pass rates

Many providers claim AI improves onboarding conversion, but the underlying technology varies. 

Asking specific technical questions helps you clarify how a vendor handles verification logic and performance optimisation.

1. Can your system show exactly why genuine users are failing?

A vendor that only reports overall pass/fail numbers can't tell you if people are dropping out over blurry uploads, thin credit files, or mismatched address formatting. You want failures broken down by step, field, and demographic.

2. Does your solution offer specific workflow recommendations, or just raw analytics?

Charts and dashboards still leave a team guessing at what to actually change. Ask if the system points to a concrete problem and solution, such as a secondary data source or a validation rule, and explains the reasoning behind it, not just the number.

3. What external benchmarks prove that higher performance is achievable?

A 75% pass rate in a new market could be strong or broken, and there's no way to tell without outside context. Ask for benchmarking against similar businesses in your sector and region.

4. Can we measure whether recommended changes increase conversion without raising fraud losses?

A completion-rate win that comes with more fraud isn't really a win. Any recommended change should ideally be tested with fraud metrics tracked alongside conversion.

Final thoughts

Some AI handles individual verification tasks like reading a document or scoring a selfie for liveness. We combine that with journey-level intelligence: our Foresight solution finds and explains where performance can improve, while our data, document and biometric tools do the automated work inside the verification itself.

Get in touch to find out more.

FAQs

How does AI help increase identity verification pass rates?

AI improves completion rates by analysing where genuine users encounter friction and optimising checks in real time. It assists users during document capture to prevent blurry images, cross-references alternative data sources for thin-file applicants and evaluates journey performance against industry benchmarks to recommend effective workflow edits.

Can AI improve pass rates without increasing fraud risk?

Yes. Improving conversion rates safely relies on better decisioning rather than lowering compliance standards. Machine learning models analyse risk signals, such as email age, device location, and cross-industry intelligence from networks like our GBG Trust, to fast-track low-risk users while reserving step-up verification for suspicious applications.

What’s the difference between point-in-time verification AI and journey intelligence?

Point-in-time AI handles specific tasks during an onboarding session, such as reading an ID card or checking a selfie for liveness. Journey intelligence analyses overall workflow performance over time, comparing pass rates against peer benchmarks to recommend structural changes to rules, data sources and routing.