AI-powered IDV platforms that recommend ways to optimise customer onboarding
Your pass rate drops five points overnight, and nobody can say why. Was it a database that's gone stale in one region? A rule that's too strict for a segment of users? By the time someone digs through the dashboards and finds the answer, the users who triggered the drop have already given up and gone somewhere else.
That lag between "something's wrong" and "here's what to change" is what a newer generation of identity verification tools is trying to fix. Instead of just reporting what happened, they analyse it and tell you what to do next.
AI-powered IDV platforms that recommend ways to optimise customer onboarding include GBG, Socure and Veriff.
Comparing providers who use AI to optimize onboarding*
|
Platform |
How it identifies optimization opportunities |
How it helps improve the journey |
|
GBG |
Analyses identity journey performance and combines it with anonymised peer benchmarking to identify where there may be room to improve |
Uses AI to recommend specific changes to configurations, data sources, data capture and capability usage, with recommendations prioritised by expected impact |
|
Socure |
Combines identity, fraud, and compliance signals and uses risk scores to determine how customers should move through onboarding |
Recommends shifts between different configurations and selectively applies OTP, document or selfie step-ups based on risk |
|
Veriff |
Uses AI-driven document and biometric verification alongside behavioural signals to identify anomalies and higher-risk customers |
Automated workflows can escalate higher-risk cases while helping refine verification flows to reduce unnecessary friction |
Why this shift is happening now
Onboarding data has never been the problem – identity teams have had dashboards for years.
What's changed is the volume and complexity of that data. Between multiple document types, biometric checks, device signals and regional data sources, there are just too many variables for a human analyst to hold in their head and reliably trace a conversion dip back to its cause.
AI-driven recommendation solutions aren't replacing the dashboard so much as doing the detective work that used to fall to a person sifting through spreadsheets.
What does an AI onboarding recommendation actually look like?
Say pass rates suddenly dip among Gen Z applicants. Rather than waiting for your team to notice the drop and go hunting for a cause, the platform compares that cohort against your historical data and anonymised peer benchmarks.
It surfaces a likely explanation, such as a credit-based data source that struggles to match younger applicants with thin credit files, then recommends switching to a data source with better coverage for that group.
From there, your team can run a controlled test, watch completion rates in real time, and confirm whether the fix actually worked.
GBG Foresight turns identity performance data into a to-do list
Our GBG Foresight solution sits on top of GBG Go, our identity orchestration platform, as an AI-powered optimisation layer that draws on insights from 800 million annual identity checks to turn raw performance data into specific recommendations.
It starts by building context: evaluating existing journey performance, layering in anonymised peer benchmarking, and flagging where completion rates lag behind similar cohorts elsewhere in the industry.
Once it identifies an issue, it recommends specific changes. These suggestions cover areas like:
- Workflow configuration adjustments to eliminate unnecessary friction for low-risk users
- Data source selection to improve match rates in specific regions
- Capability usage to ensure advanced checks like biometric verification run only when necessary
Every recommendation includes clear reasoning and supporting data points, so you can see the exact comparisons that triggered the alert. Recommendations are ranked by expected impact, and you can review them in your dashboard or get them pushed via email and Slack.
Read more: Is KYC system integration killing your business?
How we help more customers complete verification successfully
Fixing the workflow is only half the job: the other half is helping individual applicants pass on their first try.
Document capture issues are a frequent cause of drop-offs during onboarding. Our smart capture technology helps address this by guiding applicants in session with real-time feedback and step-by-step instructions. By correcting camera angles and eliminating blur or glare before an image is submitted, our system ensures clean optical character recognition and higher first-time authentication rates.
While Foresight optimises overall workflow rules across user cohorts over time, smart capture helps individual users optimise their submissions in the moment.
On the biometric side, automated facial matching compares 68 landmarks between a selfie and the portrait photo on the submitted ID. It's a place where the algorithms have a real edge over people.
As David Thomas, GBG's Global Head of Product, Documents and Biometrics, puts it: "Human beings are far more easily fooled than the facial matching algorithms we use – and that matters because every document includes a portrait photo."
Two other AI-powered IDV platforms to consider
GBG Foresight and GBG Go might not suit every setup or business model, so it's worth knowing what other options are available.
Socure
Socure uses an AI-native decisioning platform called RiskOS to manage customer onboarding.
- It evaluates identity, fraud and compliance signals to apply one-time passcodes, document requests or selfie checks based on user risk.
- A no-code workflow builder tracks conversion, completion and drop-off metrics while prefilled fields reduce user effort.
- The platform recommends adjustments between traditional, progressive, and prefilled onboarding configurations in milliseconds.
- Predictive models help businesses increase auto-approval rates while reducing manual review volumes.
Veriff
Veriff combines AI-driven document and biometric checks with behavioural data to flag anomalies during sign-up.
Veriff uses real-time risk signals to analyse incoming applications, automatically routing suspicious submissions to manual review teams. Its automated verification solution processes identity decisions in under six seconds on average.
Built-in analytics track performance across different device types and geographic markets to highlight where applicants experience friction.
The system provides ongoing recommendations to help identity teams monitor drop-off trends and refine their automated workflows on a regular schedule.
How to tell whether an AI recommendation is worth acting on
Not every automated suggestion warrants an immediate workflow change. Before adjusting your onboarding setup, evaluate recommendations against these criteria:
- Review the specific evidence and data points that generated the suggestion.
- Confirm the advice applies directly to your customer cohort rather than a generic user group.
- Calculate the expected conversion gain against the engineering time needed to make the edit.
- Run a controlled A/B test on a small percentage of traffic before deploying the change everywhere.
- Monitor completion metrics immediately after the update to verify the expected result.
The takeaway
A static dashboard can tell you that pass rates dropped. It can't tell you why, and it can't tell you what to do about it.
That's where tools like GBG Foresight come in, turning identity performance data you already have into recommendations you can actually act on.
To learn more about GBG Foresight, get in touch.
FAQs: AI-powered IDV platforms
What's the difference between an IDV dashboard and an AI recommendation tool?
A standard IDV dashboard displays historical metrics like total pass rates and drop-off counts. An AI recommendation tool analyses performance against industry benchmarks and suggests specific configuration or data changes to improve completion rates.
How do AI platforms reduce drop-offs during customer onboarding?
AI platforms reduce drop-offs by adjusting verification steps based on real-time risk scores, guiding users through document capture with live feedback and routing failed checks to fallback data sources automatically.
Can onboarding recommendations be tested before a full rollout?
Most modern IDV platforms let teams test recommended workflow changes through A/B testing, giving you a chance to measure the impact on a small user segment before applying edits across all onboarding traffic.
*Disclaimer: Information relating to third-party products and companies referenced in this article is based on publicly available sources and official publications at the time of writing. While reasonable efforts have been made to ensure accuracy, product features, positioning and company information may change and GBG does not guarantee that all information remains current or complete.
Nothing in this article constitutes an endorsement, recommendation or ranking of any third-party provider. Readers should consult each provider’s official website and conduct their own assessment before making any purchasing decisions.
To request an update or correction, please contact press@gbg.com.
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