For iGaming operators, the battle between security and a seamless onboarding experience seems never-ending.
Every new verification step seems to push a player further away from making their first deposit. But what if this trade-off isn’t a rule, but rather a symptom of outdated technology?
Identity verification: from validation to authentication
An effective identity verification flow is not a monolithic process. It’s a pipeline composed of specialized modules that operate in sequence to maximize accuracy, speed, and security.
The first module addresses the main source of failure in KYC flows: the poor quality of the document image submitted by the user. Factors such as low-resolution cameras, poor lighting, glare, and lack of focus are uncontrollable variables that lead to rejection and process abandonment.
Instead of rejecting the image, an advanced system launches a real-time enhancement pipeline through:
* AI-based quality analysis models: The system first evaluates the image for common issues such as blur, excessive brightness, and low contrast.
* Reconstruction algorithms: Based on the analysis, specific algorithms are applied to digitally correct the image—removing glare, adjusting brightness and contrast, and applying sharpness filters.
The goal is to normalize the visual input, ensuring that the image, regardless of its original quality, is optimized for the next step: data extraction.
* Business impact: Immediate increase in the success rate of the first step of the funnel. Technology adapts to the user’s reality, not the other way around, enabling accessibility for the entire customer base regardless of the device used.
Module 2: data capture and analysis with advanced OCR
Text extraction via Optical Character Recognition (OCR) is just the beginning. The real issue lies in rigid string comparisons.
A system that fails to recognize that “Jose da Silva” on a document is the same person as “José Silva” on a registration form is programmed to generate false negatives.
The solution goes beyond OCR, implementing a data intelligence layer that includes:
* Algorithmic normalization: Before comparison, extracted data is normalized. The system recognizes and adjusts for common variations in Brazilian names—such as adding or removing prepositions (“de,” “da,” “dos”), abbreviations, or surname order.
* Format flexibility: The system is trained to recognize and standardize different date formats and other fields, preventing rejections due to trivial inconsistencies.
This interpretative layer transforms raw data into structured and flexible information, ready for accurate validation.
* Business impact: A drastic reduction in false-negative rates. Legitimate customers are no longer blocked by small inconsistencies, protecting revenue and improving brand perception.
Module 3: biometrics, liveness, and multivector validation
Confirming identity requires ensuring that (A) the user is a real and present human being, and (B) their face matches the document presented, which itself must be authentic.
Liveness Detection and Fraud Prevention
Passive Liveness Detection technology prevents sophisticated presentation attacks in real time, without requiring user action.
The system is designed to detect and block the following fraud attempts:
* Photos and prints: use of printed or digital photographs
* Videos and replays: displaying videos from a legitimate user on the screen of another device
* 2D and 3D masks: attempts to bypass the system with realistic masks
* Deepfakes: AI-generated video manipulations
Cross-validation and technical accuracy
The system performs “triple verification” in milliseconds:
1) The facial vector of the selfie is compared with the photo on the document (already enhanced by Module 1).
2) The document data (extracted by Module 2) is validated in real time against government databases (e.g., Receita Federal).
3) The authenticity of the document itself is verified against known patterns.
Facial recognition technology operates with over 99.7% accuracy, measured by two key technical metrics:
* False Acceptance Rate (FAR): The probability that the system incorrectly identifies an unauthorized person as a legitimate user. FAR can be configured to extremely low levels (e.g., 1 in 1,000,000) for high-security use cases.
* False Rejection Rate (FRR): The probability that the system fails to recognize a legitimate user. Algorithms are optimized to minimize friction, maintaining a low FRR without compromising security.
Verification architecture: 1:1 vs. 1:N
The solution must support two distinct use cases for different operational needs:
* Face Match 1:1 (verification): Compares a newly captured facial image with a previously registered image or biometric template. Used for login authentication, identity confirmation in transactions, or document validation. The question answered is: “Is this person who they claim to be?”
* Face Match 1:N (identification): Compares a new facial image with a biometric database to find a match. Used for identifying customers in physical environments, detecting duplicate registrations, or fraud investigations. The question answered is: “Who is this person?” High-performance architecture allows 1:N searches among millions of faces in under one second.
* Business impact: Maximum security and compliance, proactively mitigating fraud and ensuring adherence to strict regulatory standards. The robustness of the process builds a safe and reliable user base.
Module 4: efficient performance and the power of the network effect
How can you scale user acquisition in a market where most players already belong to other platforms? Forcing an already verified user to repeat the document submission process is the biggest source of friction in onboarding.
The implementation of a reusable identity ecosystem works as follows:
* Verified user identification: When a new user starts registration, the system checks (via CPF or other data) whether they have already completed a full KYC process on any partner platform within the same verification network.
* Fast reauthentication flow: Instead of requesting documents again, the flow is simplified to a new selfie with liveness detection. The system performs a Face Match to confirm that the user is the same as the previously validated identity.
* Business impact: Reduces onboarding time by up to 90% for most of the target market. Conversion becomes almost instantaneous, dramatically accelerating growth and market share capture.
How a system turns architecture into KPIs
The superiority of a Face Match solution is not abstract, it’s measurable and directly reflected in the performance indicators that define iGaming success.
A cutting-edge architecture like the one detailed above is not a cost, but a direct investment in optimizing key KPIs:
* Automated approval rate: >99%
* Average onboarding time: <30 seconds
* KYC funnel abandonment rate: x% reduction (to be confirmed)
* False-negative accuracy index: 99.7%
Ultimately, technology stops being a compliance cost center and becomes the main driver of acquisition, conversion, and customer retention.
Caio Brisola
Head of Product at Legitimuz, specialist in identity verification and digital compliance solutions. The company provides advanced AI-powered technologies designed to prevent fraud and optimize KYC (Know Your Customer) and onboarding processes, with a focus on security, accuracy, and regulatory compliance.
