Criteria for choosing an anti-fraud platform
Choosing an anti-fraud system for a business is a task that directly affects the company’s financial security and customer trust. According to the Russian Ministry of Internal Affairs (MVD), the damage from IT crimes in January–August 2025 amounted to 134 billion rubles. Moreover, the growth in damage compared with the same period a year earlier reached 15%. In these conditions, a sound approach to choosing a protective solution becomes a strategic priority. Let’s look at the key parameters worth paying attention to.
Detection accuracy and the false-positive rate
Accuracy is the main indicator of an anti-fraud solution’s effectiveness. Modern machine-learning-based systems reach accuracy figures of 99% and above on the ROC-AUC metric. However, high fraud detection accuracy must be combined with a low false-positive rate (False Positive Rate). Every mistakenly blocked transaction is lost profit and an unhappy customer.
When evaluating a solution, ask for specific metrics: the false-positive rate, the percentage of fraudulent operations detected, and figures on real data rather than only on test samples. A quality anti-fraud solution keeps the error below 0.1% while maintaining a high threat detection rate. The system’s ability to adapt to the individual behavior patterns of customers also matters — this reduces the number of false blocks for users with atypical but legitimate habits.
Transaction processing speed
Anti-fraud must work in real time without creating delays for legitimate payments. Modern solutions make a decision on a transaction in 10–300 milliseconds. Leading platforms deliver a response time of up to 10 ms at a throughput of more than 100,000 requests per second. For a business with a high volume of operations, the TPS (Transactions Per Second) figure is critically important — the number of transactions the system can process per second.
A delay in processing a payment, even of just a few seconds, can cause a buyer to abandon the purchase. For this reason, when choosing a solution, run load testing under conditions as close as possible to your business’s peak loads — seasonal sales, marketing campaigns or the hours of highest activity.
Types of fraud detected
Fraud schemes are constantly evolving. A modern anti-fraud platform must detect a wide range of threats: theft of payment data, identity substitution, phishing attacks, the use of compromised accounts, device-spoofing attacks. Special attention should be paid to deepfake protection — deepfake recognition technology with 99.3% accuracy makes it possible to detect synthesized videos and images that are increasingly used to bypass biometric verification.
Liveness Detection protects against attacks using photos, video recordings or 3D masks. Comprehensive solutions combine active and passive verification methods: the passive one analyzes the image for artifacts, while the active one asks the user to perform a simple action. The system’s ability to detect fraud at the session level also matters — anomalous user behavior, remote control of the device, the presence of malware.
Integration options
Ease of implementation determines how quickly protection can be launched and how much it costs to maintain. The most flexible solutions offer integration via a RESTful API, ready-made SDKs for mobile and web applications, and the ability to connect seamlessly to existing payment systems. Integration time ranges from a few minutes to a few days, depending on the complexity of the infrastructure.
When choosing a solution, clarify the available deployment options: cloud (SaaS), on-premise or hybrid. Cloud solutions provide a quick start and require no infrastructure of your own. On-premise deployment gives full control over the data and may be a mandatory requirement for organizations that work with sensitive information. Assess the quality of the documentation, the availability of a sandbox for testing, and the vendor’s integration support.
Scalability for load growth
A business grows, and with it the volume of transactions increases. An anti-fraud platform must scale horizontally without degrading performance. Make sure the chosen solution can handle multiples of the current load — for example, when entering new markets or during seasonal sales growth.
The system architecture must allow computing resources to be added without stopping the service. For cloud solutions, clarify the terms of automatic scaling and its effect on cost. For on-premise, clarify the infrastructure requirements when the load increases two-, five- or tenfold. Also assess how the system copes with peak loads: whether it works correctly when the design figures are briefly exceeded.
Compliance with regulatory requirements
Regulatory requirements for data protection and fraud prevention are tightening. In Russia, Federal Law 152-FZ “On Personal Data” and the Bank of Russia’s requirements for the anti-fraud systems of financial institutions are in force. From March 1, 2026, the state information system “Antifrod” will be fully operational, which will expand the criteria for identifying suspicious operations. When working with international clients, it is necessary to take into account the requirements of GDPR, PCI DSS for payment data, as well as KYC/AML standards.
Failure to meet regulatory requirements entails fines of up to tens of millions of rubles, the revocation of licenses and reputational losses. Choose solutions certified to work with personal data and biometric information. Clarify where the data is physically stored, how it is protected, and what mechanisms are provided for meeting regulators’ requirements — generating reports, notifying about suspicious operations, and interacting with government systems.
Pricing model
The cost of an anti-fraud solution is made up of several components: a fee for checking transactions, a subscription fee for access to the platform, and the cost of integration and support. Common models are pay-per-check (from a few rubles per operation), a fixed subscription for a certain volume of transactions, or a combined approach.
Pricing transparency is an important selection criterion. Make sure there are no hidden charges for additional features, exceeding limits, or technical support. Compare the total cost of ownership (TCO) for different solutions, taking planned business growth into account. For some companies, the optimal model is one with no subscription fee and payment only for the checks actually performed — this makes it possible to forecast costs accurately and avoid overpaying during periods of low activity.
Technical support and SLA
Anti-fraud is a critical system whose failure directly affects business processes. Assess the level of technical support: response time to requests, 24/7 availability, communication channels. Having a dedicated account manager speeds up the resolution of non-standard issues and helps configure the system to the specifics of the business.
Pay attention to the terms of the SLA (Service Level Agreement): the guaranteed level of service availability (99.9% and above), the maximum downtime, and compensation in case the agreements are breached. Clarify the incident escalation procedure and the timeframes for resolving critical problems. A trial period is an opportunity to assess the quality of support in practice before signing a long-term contract. Responsible providers offer trial access lasting from two weeks to a month with full functionality and dedicated support.
Business use cases
An anti-fraud platform is not a universal tool with a single mode of operation. Its value comes through in specific business processes where the risks of fraud are highest. Let’s look at four key scenarios in which such systems deliver a measurable result.
Protecting payment operations
Online payments remain the main target for attackers. According to the Bank of Russia, in 2024 fraudsters stole a record 27.5 billion rubles from Russians’ accounts — 74% more than a year earlier. At the same time, the number of fraudulent operations with bank cards reached 821,870 cases. Through the Faster Payments System (SBP), 8.2 billion rubles were stolen.
An anti-fraud system analyzes each transaction in real time, assessing dozens of parameters: the payer’s IP address and geolocation, the device fingerprint, the nature of behavior on the site, and how well the amount matches the typical average transaction value. Operations receive a label — safe, requiring review, or subject to blocking. The decision is made in fractions of a second: modern systems process a transaction in 0.1–0.3 seconds without creating delays for honest buyers.
For online stores and payment services, the balance between security and conversion is critical. Overly aggressive rules lead to false blocks and lost customers. Flexible configuration of anti-fraud rules to fit the specifics of the business makes it possible to minimize both risks: letting fraudulent operations through, and unjustified refusals of legitimate payments.
Customer verification during onboarding
The registration stage for a new user is an entry point for both real customers and fraudsters. Synthetic identities, forged documents, the mass creation of fake accounts — these threats require comprehensive verification before a person gains access to the service.
A modern KYC process combines several levels of verification. The first is automatic document recognition (AI-OCR) with an authenticity check: the system analyzes security features, the machine-readable zone (MRZ), and detects traces of digital image processing. The second level is biometric identification: matching a selfie or video with the photo in the document. Face recognition accuracy in leading solutions reaches 99.7%, which practically rules out identity substitution. The third level is a check against external databases: sanctions lists, registries of invalid passports, credit bureaus.
Automation reduces verification time from several hours with manual checking to a few seconds. This directly affects conversion: every additional step in the registration process reduces the share of completed applications. According to industry research, quality KYC automation increases conversion by 10–15% while at the same time reducing the risk of letting fraudsters through.
The liveness check — confirmation of a person’s “live” presence — is especially important. It protects against the use of photos, video recordings and deepfake forgeries. Modern algorithms detect deception attempts with an accuracy of up to 99.9%, analyzing micro-movements, the reflection of light in the eyes and other biometric markers.
Loyalty program monitoring
Bonus programs and cashback systems were created to increase customer loyalty, but they have become an attractive target for fraudsters. Experts estimate that fraud in loyalty programs affects 3–5% of the total volume of transactions. With a large-scale bonus program, direct losses can amount to tens of millions of rubles a year.
| Category | Description |
|---|---|
| Typical fraud schemes include several directions: | Internal fraud: employees run customers’ purchases through their own loyalty cards, appropriating the points earned. Mass creation of fake accounts: fraudsters register hundreds of accounts to obtain welcome bonuses, which are then resold or cashed out. Account hijacking: attackers gain access to the accounts of real customers and spend their accumulated points. |
| An anti-fraud system for loyalty programs detects anomalies in participants’ behavior: | an atypical purchase frequency, mass redemption of bonuses, a sharp change in activity patterns. Practice shows that introducing such monitoring can reduce the level of fraud by tens of times. For example, one large retailer, after launching a control system, cut the number of loyalty-card abuses by a factor of 20 — from thousands of cases to a few dozen a year across the entire network. |
In addition to direct financial losses, fraud in loyalty programs causes reputational damage. Customers who discover the unauthorized redemption of their points lose trust in the brand. Transparency of operations — notifications about accruals and redemptions, detailed breakdowns in the personal account — simultaneously improves security and strengthens loyalty.
Detecting internal fraud
Threats come not only from outside. According to research, 60% of companies have encountered cases of corporate fraud. The participants in these schemes are most often middle managers (73% of cases) and rank-and-file employees (70%). Direct financial damage is the most common consequence, reported by 83% of the affected organizations.
Internal fraud covers a wide range of violations: theft of physical assets, manipulation of financial reporting, kickbacks in procurement, misuse of corporate cards, leaks of confidential data. The key difficulty is that employees have access to systems and information as part of their job duties, which disguises unlawful actions as ordinary work activity.
An anti-fraud platform for countering internal threats works differently from payment protection systems. It analyzes employees’ actions in corporate systems, detecting deviations from typical behavior: unusual activity times, access to data outside one’s area of responsibility, anomalous volumes of information downloads. The system builds a profile of each user’s normal behavior and flags significant deviations.
Effective countermeasures require a combination of technical and organizational measures. According to surveys, 76% of companies consider internal audits the most effective tool for detecting fraud. In second place is an anonymous whistleblowing system (40%). Among preventive measures, the leaders are counterparty trustworthiness checks (82%) and financial control of operations. An anti-fraud platform becomes the technological foundation that unites these tools into a single monitoring and response system.
An anti-fraud platform for business delivers results when it is chosen for specific risks and built into key processes without a drop in speed and conversion. That is why, when evaluating a solution, it is important to keep a balance between accuracy and false positives, response time, coverage of fraud schemes, ease of integration and deployment, scalability, compliance, transparent pricing and quality of support.
Start with a priority scenario — protecting payment operations, KYC onboarding, loyalty program monitoring or reducing internal risks — and test how the solution works on your own data and peak loads during the trial period. This approach helps configure rules and controls so that protection works reliably and expands together with the growth of the business.
