What anti-fraud is
The term “anti-fraud” comes from the English anti-fraud, which literally translates as “countering fraud”. It refers to the totality of technologies, algorithms and procedures aimed at detecting, preventing and blocking fraudulent actions in the digital environment.
Anti-fraud systems analyze every operation in real time. They assess many parameters: who is performing the action, from where, from which device, and whether this matches the user’s usual behavior. Based on this analysis, the system makes a decision — let the transaction through, request additional confirmation, or block it as suspicious.
The scope of anti-fraud extends far beyond the banking sector. Such solutions are actively used by payment services, online stores, marketplaces, insurance companies, online booking platforms and even dating services. Wherever financial flows pass or personal data is processed, anti-fraud becomes a critically important element of protection.
The need for such systems is driven by the growth of cybercrime. According to the Central Bank of Russia, in the second quarter of 2024 alone the volume of operations carried out without customers’ consent amounted to around 4.8 billion rubles. Attackers are constantly improving their attack methods: from ordinary phishing to complex schemes using social engineering and deepfake technologies. Anti-fraud is the business’s response to these threats.
The value of anti-fraud is measured not only in prevented losses. The system protects the company’s reputation, increases customer trust and ensures compliance with regulatory requirements. In Russia these are the provisions of Federal Law 152-FZ on personal data and the requirements for countering the legalization of proceeds obtained by criminal means. At the international level — the KYC, AML and GDPR standards.
For end users, anti-fraud works almost imperceptibly. An additional request for a code from an SMS, confirmation via a push notification or face recognition when logging into an app — these are all elements of anti-fraud protection that do not complicate the interaction with the service but significantly reduce the risk of falling victim to fraudsters.
How anti-fraud works
An anti-fraud system analyzes every user action in real time and, in fractions of a second, decides: let the operation through, request additional confirmation, or block it. For an ordinary customer this process goes unnoticed, while a fraudster runs into an obstacle that makes the attack unprofitable.
Data collection and analysis. When an operation is performed, the system instantly collects dozens of parameters: the amount and time of the payment, the IP address, geolocation, device characteristics, and the history of the user’s previous actions. This data is compared with the customer’s typical behavior. If a person usually makes purchases during the day from Moscow for amounts up to 5,000 rubles, but is now trying to pay 150,000 rubles at night from an IP address in another country, the system flags an anomaly.
Risk scoring. Each operation is assigned a numeric score based on many factors. A new user, a large amount, a suspicious IP, a mismatch between the card’s country of issue and the payer’s location — all of this increases the final score. The higher the score, the more likely the fraud. Modern systems take hundreds of parameters into account at once and build an individual behavior profile for each customer.
Fingerprinting. The technology creates a unique digital fingerprint of a device from the totality of its characteristics: screen resolution, installed fonts, browser version, time zone, system language. Even if an attacker changes their IP address and creates a new account, their device can be recognized by this fingerprint. Behavior is analyzed separately: typing speed, the nature of mouse movement, the time between actions. A sharp deviation from the usual patterns signals a possible substitution of the user.
Machine learning. The algorithms are trained on millions of real operations, revealing non-obvious patterns in the behavior of fraudsters. The models achieve an accuracy of detecting suspicious operations of up to 95% with a minimum of false positives. Unlike rigid rules, an ML system adapts to new deception schemes and takes into account the individual habits of each customer. If a person regularly makes purchases abroad, the system will not block such payments without good reason.
Making the decision. Based on the analysis, the system assigns the operation to one of the categories:
- Low risk — the operation goes through without additional checks, which speeds up the process for honest customers.
- Medium risk — confirmation is requested via an SMS code, a push notification or biometrics using 3D Secure technology.
- High risk — the operation is blocked or passed to a specialist for manual review.
External sources. Anti-fraud consults databases of compromised cards, fraudulent accounts, and suspicious IP addresses. In Russia, banks exchange information through the Bank of Russia’s platform. According to the regulator, in 2024, thanks to anti-fraud systems, it was possible to prevent 72.17 million fraudulent operations and save customers 13.5 trillion rubles — more than twice as much as a year earlier.
Balancing security and convenience. The key task of anti-fraud is not to create absolute protection but to make fraud economically unprofitable. Rules that are too strict will scare off honest customers, while rules that are too lenient will let attackers through. That is why the system constantly balances between two types of errors: false positives, when a legitimate operation is blocked, and letting real fraud through. A quality anti-fraud solution minimizes both indicators, applying strict measures only to genuinely suspicious cases.
Modern anti-fraud solutions work in a comprehensive way: scoring provides a quantitative risk assessment, fingerprinting links actions to specific devices, machine learning detects complex patterns, and integration with external databases keeps the data current. Together these technologies form a multi-level protection that adapts to the constantly changing methods of fraudsters.
How to prevent online fraud
According to the NAFI Analytical Center, in 2025 94% of Russians encountered fraud attempts. At the same time, the damage from cybercrime for January–August amounted to 134 billion rubles. These threats can be countered on two levels: on the business side — by implementing technological solutions, and on the user side — by following digital security rules.
Anti-fraud solutions for business
Modern anti-fraud systems combine several technologies that work in tandem and reinforce each other.
| Category | Description |
|---|---|
| Biometric verification and Liveness Detection: | Liveness-check technology determines whether there is a real person in front of the camera or an imitation — a photo, a video recording, a 3D mask. The system analyzes skin structure, micro-movements of the eyes, and the three-dimensional map of the face. Solutions certified to the ISO/IEC 30107-3 standard reduce the risk of fraud to 99%. |
| Deepfake detection: | With the rise of generative AI, a new type of attack has emerged: fraudsters create synthetic videos that imitate the voice and face of a real person. According to the Wall Street Journal, companies’ losses from deepfake attacks in the first quarter of 2025 exceeded $200 million. Modern detectors recognize all the main types of forgery — from face swapping to lip synchronization — with an accuracy of more than 99%. |
| Intelligent document processing (AI-OCR): | The algorithms recognize and verify documents: they check the machine-readable zone, security features, and data integrity. This rules out forged passports, driver’s licenses and other IDs during remote identification. |
| Screening against databases: | AML checks automatically compare the customer against sanctions lists, debtor registries, and the databases of the Federal Tax Service (FTS), the Ministry of Internal Affairs (MVD) and other agencies. This makes it possible to identify dubious counterparties before a deal is made. |
| Behavioral analysis: | Machine learning records the user’s typical behavior: activity times, devices, geolocation, the nature of transactions. Deviations from the usual pattern trigger an additional check or a block of the operation. |
When choosing a solution, it is worth paying attention to processing speed (less than a second per check), the accuracy of the algorithms (confirmed by independent NIST or iBeta tests), compliance with the KYC/AML requirements and Federal Law 152-FZ, as well as ease of integration via an API.
Security rules for users
Technical protective measures on the part of business and the state do not eliminate personal responsibility. Most successful attacks are still built on social engineering methods, where the victim themselves hands the attacker access to their data.
Effective protection against online fraud is built on a combination of technology and mindful behavior. Business implements intelligent verification systems, the state creates a regulatory infrastructure, and users follow the basic rules of digital hygiene. Only with such a comprehensive approach is it possible to significantly reduce the risks and maintain control over your data and finances.
Online fraud can be kept in check when anti-fraud assesses the risk of actions in real time and tightens verification only where behavior, the device or the data looks atypical. This approach reduces losses and protects trust in the service without turning every operation into a complicated procedure for the honest user.
The maximum effect comes from comprehensive protection: business builds multi-level checks, the state maintains a regulatory infrastructure, and users follow the basic rules of digital security. In this combination, anti-fraud helps maintain control over data and finances even when fraudsters constantly change their attack scenarios.
