Even if you automate most of your onboarding journey, your manual review queue can still pile up. A minor address typo, a low-light selfie, or an overly conservative risk rule can instantly route a genuine customer to a human analyst, and now your team is spending its time clearing straightforward applications that should never have landed there in the first place.
The fix isn't more automation in general; it's automation aimed at the specific points where cases fall out of the automated path, so review gets reserved for the applications that actually need a person's judgment.
AI-powered identity verification providers that help reduce manual reviews include GBG, Socure and Veriff.
|
Provider |
Automated verification capabilities |
How it reduces manual review |
|
GBG |
Multi-source data verification, document authentication, face matching, passive liveness and automated decisioning |
Routes clear cases automatically and sends partial matches through secondary automated checks |
|
Socure |
AI identity matching, risk scoring, document verification and automated decisioning |
Applies risk and reason codes to automate exception processing and provides matched identity records to streamline reviews |
|
Veriff |
Automated document, biometric and fraud-signal analysis with human review reserved for ambiguous cases |
Uses automated analysis and real-time user feedback during capture to resolve straightforward cases |
It's rarely the case that a platform lacks automation altogether. More often, review happens because a small friction point at one stage of the journey triggers a fallback to a human operator, and the solution to the problem looks different at each stage.
At the identity data stage, a weak or incomplete match is the usual trigger. Solutions to this involve pulling from multiple data sources and matching on more granular attributes (rather than accepting a single thin file as a dead end).
With documents, it's typically poor image capture or a check that can't confirm authenticity on its own. Real-time capture guidance and automated forensic checks help.
Biometric review usually comes down to an unclear face match or liveness the system can't confirm, which algorithmic matching and passive liveness detection are built to resolve without a human ever looking at the image.
Conservative decisioning rules push borderline cases to a reviewer by default, when risk-based logic could route a partial match to a secondary automated check instead.
What’s more, sometimes the real problem isn't any single stage; it's that nobody on the team can tell why review volume went up, which is where continuous performance tracking and automated analytics come into play.
Read more: How data verification powers fast, secure onboarding
Here’s a look at how we help you reduce manual review volumes throughout the customer lifecycle:
Unnecessary manual reviews often start at the data verification stage when a customer record returns an incomplete match. Our data verification capabilities assess how identity elements match across independent sources and return outcomes such as match, partial match and no match.
Instead of treating every non-perfect result identically, partial matches can be routed automatically to a secondary verification source or alternative attribute combination. This process establishes confidence in a match without involving an operator.
Additionally, these verification outputs can be used alongside fraud scores from our GBG Trust network to route customers directly to accept, reject or manual review based on custom risk thresholds.
When a user needs to submit physical documentation, manual queues often back up due to poor image quality or slow processing times. Our GBG Go platform automates document capture and authentication by applying over 50 forensic authentication tests in seconds against a library of 8,500+ government-issued IDs across 195 countries.
Document verification takes an average of five seconds to complete, with 99.5% of customer journeys handled directly by backend APIs on the first pass.
For biometric checks, automated facial recognition technology compares the biometric information from an identity document to a selfie, generating an instant face-match score. Passive liveness technology assesses whether the submitted selfie represents a live person rather than a spoof attempt, requiring no complex movements from the user.
Reducing manual review also requires understanding why referrals happen in the first place. Our AI intelligence layer, GBG Foresight, tracks onboarding performance continuously as it happens rather than requiring compliance teams to wait for periodic manual reviews.
Automated alerts flag abnormal changes in pass rates or traffic volume across channels and surface potential causes. This helps you identify whether a sudden spike in manual reviews is caused by a broken form field, a new document format or a change in fraud patterns, without doing that work “by hand”, as it were.
If you want to evaluate other options, here are two alternatives to consider.
Socure runs identity verification and fraud decisioning through a predictive machine learning platform, evaluating digital identity elements (such as name, address, phone, email and Social Security number) alongside document authentication.
Reason codes and risk scores automate exception handling for the clear-cut cases, and when a case does need a person, Socure puts the applicant's input side-by-side with matched identity records so the review itself goes faster.
Socure's predictive models aim to replace static rules-based logic, reducing both false positives and false negatives.
Veriff provides an automated identity verification platform that combines document reading, biometric analysis and fraud signal detection. The platform is built to handle high-volume onboarding across global markets while keeping human review reserved for more ambiguous cases.
Real-time feedback given during document capture, such as catching blur and poor lighting before submission, is what keeps a case from landing in a review queue over something easily fixable (such as a poorly taken photo). This guidance helps many users complete verification on their first attempt.
Eliminating manual review entirely is rarely the right objective for a compliance or fraud team. After all, certain high-risk, complex or truly ambiguous applications require human scrutiny to protect your business.
The goal is to determine if the cases landing in your review queue are the ones that actually belong there, or just routine exceptions an automated check could have resolved on its own.
To evaluate if your manual review process is working efficiently, track these metrics:
Automating identity verification isn't about removing humans from the loop entirely but ensuring they only inspect applications that actually warrant human judgment.
Granular data matching, automated document and biometric checks and risk-based routing are how platforms like GBG Go help you get there.
To find out more, get in touch.
High manual review rates are usually caused by rigid verification workflows, poor data coverage, or weak document capture tools. When a platform can’t resolve partial data matches or read a blurry document upload automatically, it defaults to sending the application to a human reviewer.
AI reduces manual reviews by using predictive risk scoring, passive liveness detection and multi-source data matching to evaluate identities more accurately. By analysing subtle fraud signals and cross-referencing global datasets in real time, AI can approve genuine users instantly while isolating true fraud risks for review.
Static workflows send every applicant through the exact same sequence of verification checks regardless of risk level. Dynamic routing uses real-time risk signals to adapt the onboarding path, fast-tracking low-risk users through basic checks while automatically triggering step-up authentication only for higher-risk cases.
*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.
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