The choice of a biometric data storage model in KYC systems determines a company's risk profile, the level of control over information, and compliance with regulatory requirements. Local hosting provides full autonomy and protection against cross-border transfers, but requires one's own infrastructure and cybersecurity expertise. Cloud solutions speed up implementation and simplify scaling, while creating dependence on the provider and questions about the jurisdiction of the data. In this article we break down where biometrics is physically stored in both models, what requirements Federal Law 152-FZ and GDPR impose, compare the threat profiles of each approach, and provide criteria for choosing the optimal strategy for a specific business.
Traditional customer verification requires significant resources and time, and complex onboarding processes lead to the loss of up to 63% of potential users at the registration stage. Artificial intelligence radically changes the approach to KYC — neural networks automate document recognition, biometric identification, screening against sanctions lists, and fraud detection, reducing costs tenfold and speeding up verification to mere seconds. In practice, this is usually implemented as a set of modules that can be embedded into onboarding via an SDK and API: AI-OCR for documents and the MRZ, matching the face with the document (face-match), a liveness check, AML/sanctions screening, and anti-fraud. For example, in the NeuroVision lineup these tasks are covered by the combination of IDP/AI OCR, Enface (face verification), IDP Liveness, AML, and an anti-fraud module — they can be used separately or as a single KYC+AML loop. In this article we take a detailed look at the architecture of AI KYC solutions, the machine learning algorithms at each stage of the process, and the practical steps of implementation — from the formulation of requirements to the industrial launch.
An anti-fraud API is a layer that receives data about the user and the transaction, assesses the probability of fraud in a time imperceptible to the customer, and returns a verdict together with an explainable risk score. In this article we break down real-time fraud detection and behavioral scoring (risk scoring): which signals to transmit, how to read the risk factors, and where to set the approve/review/decline thresholds in order to reduce fraud while maintaining conversion.
Anti-fraud cannot be implemented "at the push of a button": without a pilot and a shadow mode, the system will either start mistakenly blocking bona fide customers or miss attacks. This article offers a clear route from preparing the team, the data, and the initial rules to assessing the key metrics (false positive, detection rate, response time). We show how to roll the solution into production and enable blocks gradually, maintaining the balance between security and conversion.
The price of an anti-fraud system is not only the license or the per-check fee: the final cost is affected by integration, configuration, infrastructure, support and the team's resources. In this article we break down the components that make up the total cost of ownership and what to look at when choosing SaaS or on-premise. And we show how to calculate the ROI of anti-fraud — so you can compare options, justify the investment, and understand when protection really pays off.
An antifraud solution delivers a stable effect only under three conditions: high-quality input data, seamless integration into your processes, and security at the level of regulatory requirements. This article offers a practical checklist: what to collect for scoring, how to connect the system via API/SDK or connectors, and which protective measures are mandatory for transmission and storage in light of Federal Law 152-FZ, GDPR and PCI DSS.
An anti-fraud platform for business is needed wherever money, bonuses and access to a service move online: fraudsters act faster than manual rules can be updated. This article covers the key selection criteria (accuracy and false positives, speed, integration, compliance with regulatory requirements) and use cases with a measurable effect: payments, KYC onboarding, loyalty programs and internal risks.
Anti-fraud today is neither a "black box" nor a list of rules, but a technological architecture that collects signals from transactions, devices and behavior, assesses risk in streaming mode and makes a decision before the operation is even completed. In this article we break down the modules that make up such a protection loop and how to build real-time fraud monitoring: from data collection and enrichment to risk scoring, automated response and escalation.
Online services rest on trust: a single successful attack can cost money, personal data and reputation. Anti-fraud protection helps stop fraud at an early stage — in real time it assesses the level of threat and cross-references user behavior, device parameters, document data and biometrics. Below we explain how anti-fraud works and which measures help reduce the likelihood of deception for businesses and users.
The choice of a biometric data storage model in KYC systems determines a company's risk profile, the level of control over information, and compliance with regulatory requirements. Local hosting provides full autonomy and protection against cross-border transfers, but requires one's own infrastructure and cybersecurity expertise. Cloud solutions speed up implementation and simplify scaling, while creating dependence on the provider and questions about the jurisdiction of the data. In this article we break down where biometrics is physically stored in both models, what requirements Federal Law 152-FZ and GDPR impose, compare the threat profiles of each approach, and provide criteria for choosing the optimal strategy for a specific business.
Since May 30, 2025, fines for a personal data leak in KYC processes have reached 15 million rubles for a single incident, and for repeat violations — up to 500 million or 3% of annual revenue. Biometric data, which cannot be "changed" like a password, has become the main target for fraudsters who use deepfake and synthetic identities to bypass verification. At the same time, the requirements of Federal Law 152-FZ for operators have been tightened: localization of data within the Russian Federation, mandatory encryption of transmission channels, separate storage of biometric templates, and documented recording of every processing stage. Companies implementing KYC solutions face the need to build multi-level personal data protection — from technical security measures to organizational procedures and the choice of a reliable provider with confirmed certificates and experience working under strict regulatory requirements.
Entering international markets requires enhanced customer due diligence: in 2025, regulators tightened the KYC requirements for non-residents, made in-depth EDD verification mandatory, and increased control over sanctions compliance. Standard identification is not enough — companies need a comprehensive approach with jurisdiction risk assessment, verification of the sources of funds, and continuous monitoring. We break down the current KYC 2025 requirements for international clients and non-residents, the mandatory set of data and documents, a step-by-step online verification scheme, and technologies for automating global checks.
