BI.ZONE AntiFraud
Monitors financial and nonfinancial operations (e.g., personal data access, contact detail change) in real time to identify fraud schemes that spread across different channels
Counters little-known schemes with a fraud analytics model drawing on the experience of the largest organizations
Tracks illegal login attempts based on common indicators of fraudulent authorization and deviations from typical customer behavior
Detects multi-accounting, device emulation, and logins from ill-reputed devices.
Tracks automated user activity, generates ID session detections, and forwards them to WAF
Enables seamless login experience through RBA
Identifies and blocks fraudulent financial and nonfinancial operations, drawing on expert rules and AI models trained to detect abnormal user behavior. Supports multiple transaction channels for comprehensive cross-channel monitoring
Automatically blocks fraudulent transactions with a response time as low as 0.1 seconds
Features ready-made rules and rule creation capabilities. Supports customization of fraudulent transaction response scenarios. Allows users to generate reference books and additional parameters for future rules and AI models
Leverages an AI model for user profiling to reduce false positives and identify emerging fraud schemes
Allows users to test rules and aggregates on loaded transaction pools before deployment as well as to analyze the test results
Analyzes over 350 mobile device and web application parameters in real time to detect anomalies. Builds global user profiles, enhancing the effectiveness and accuracy of fraud detection
Assesses device legitimacy, supported by a global reputation database
Analyzes and stores key user behavioral characteristics registered during online service sessions
Identifies potential fraud indicators for detailed analysis. Key detections include remote connections, active calls, malware presence, VPN/Tor usage, credential spoofing, robotic activity, and more than 80 additional criteria
The entire deployment procedure comes down to running a few installation packages, which significantly reduces costs and implementation time
Employees log in using their corporate credentials, which streamlines user onboarding and permissions management
The system automatically suggests input options in English, eliminating the need to know exact attribute or function names
Users can leverage ready-made AI models or upload their own based on the XGBoost algorithm for more efficient antifraud automation
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Fraud is a cybercrime that typically targets a victim’s money. Essentially, it comprises online fraud, mostly in the banking sector. Adversaries often rely on social engineering, carding (using stolen card data), and other tools like trojans.
Antifraud solutions help credit institutions and online services protect their customers from such threats. These products rely on specific indicators to detect suspicious transactions, assess their legitimacy, and suggest further actions. Additionally, such solutions ensure anti-money laundering (AML) and prevent terrorist financing.
Banks usually employ transaction and session antifraud products.
- Transactional antifraud verifies each customer action (e.g., authentication, balance inquiry, or payment). By integrating sets of parameter, banks can ensure that their antifraud solutions decide whether to allow, block, or forward a transaction for manual review.
- Sessional antifraud monitors customer behavior within an online service based on several parameters. In case of anomalies, the solution reports the illegitimate activity to the bank.
BAF combines both approaches.
Antifraud is also relevant for online stores, insurance companies, loyalty program initiators, and government agencies. Generally, any online service would benefit from antifraud to prevent account theft, detect criminal accounts, block bot traffic, or simplify customer authentication.
Several key parameters should be factored in.
The product should be able to build a unified payment profile for each customer. This ensures multi‑channel transaction analysis and prevents fraud schemes that spread across different service channels. Cross-channel antifraud platforms are best suited for this purpose.
The solution should also feature customizable or adaptive rule management capabilities for quicker deployment of new rules and, hence, faster response to emerging fraud.
An antifraud platform should enable ML‑based risk assessment to enhance rule efficiency.
A vendor’s team should have proven experience working with large organizations.
Machine learning models are certainly resourceful. They reduce false positives and recurrent triggering, continuously learn from new attacks, and highlight risk levels for suspicious transactions. However, this is not a silver bullet—effective antifraud needs to combine ML capabilities, sets of rules, and human expertise.
Cloud antifraud platforms are perfect for smaller companies as they entail lower hardware costs. This option is inherently secure: customer personal data is not shared externally, sensitive information is hashed, and all traffic is transmitted over encrypted channels.
On-prem solutions are deployed within an organization’s IT perimeter. They are better suited for large businesses that require full control over their data assets.
Vendor availability and prompt support are crucial as they allow organizations to:
- quickly fix solution bugs
- add new payment types and features
- get expert advice on the correctness of fraud detection rules
There is no universal set of parameters that works for every company. However, most businesses stick to the following criteria:
- Share of false positives. This is one of the key metrics to track. It shows the portion of false positives out of the total number of transactions for a specific period. Numerous false positives increase the workload on the call center and financial monitoring team. This also adversely affects customer experience as they have to contact the bank and prove the legitimacy of their transactions.
- Share of fraud prevented. Simply put, the more money an antifraud system protects from fraudsters, the better. Ideally, the solution should show what portion of transactions each rule blocked. This makes it possible to evaluate the effectiveness of specific rules.
- Rule implementation time. The more efficiently new rules (including complex ones) can be configured in an antifraud product, the quicker an organization can respond to emerging fraud schemes.
- A customer performs an action (e.g., logs in, makes a service payment, attempts to purchase a product).
- Your automated systems (AS) forward the transaction to BAF for analysis. The solution carries out customer and device profiling based on the related data.
- BAF assesses the transaction’s risk by analyzing whether this user behavior is consistent with the existing profile.
- Drawing on the risk assessment results and a set of rules, BAF issues a verdict: allow, suspend, or block the transaction. This verdict is then returned to your AS.
- Depending on the verdict, the AS either allows the transaction or rejects/suspends it and notifies the customer.
- If the transaction is rejected/suspended, the solution logs an incident. A fraud analyst then confirms or refutes fraudulent activity.
- Transaction handling and incident investigation results are used for the daily retraining of mathematical models.
Each BAF module receives and processes hundreds of gigabytes of data every day to ensure transaction security.
- The transaction module analyzes data used within a transaction. The list of collected information is agreed upon with the bank or online service during BAF deployment. This may include details about a customer’s device, internet connection, and the transaction itself (e.g., amount, recipient).
- The session module requests data like a phone number, IP address, or type of network connection to create a digital fingerprint of a customer’s device. This enables the immediate detection of anomalies that may not be apparent during a stand-alone transaction. Additionally, the module detects atypical mouse movements or abnormal typing speed.
BI.ZONE AntiFraud can be integrated with any remote banking or processing system through the API. Out-of-the-box integrations are already available for Financial CERT (Financial Computer Emergency Response Team, a special division of the Bank of Russia) as well as Way4 and Compass Plus processing centers. BAF is a comprehensive solution that can also be integrated with various client platforms (banks or payment aggregators), providing reliable customer protection.
Integration with the BI.ZONE ecosystem further enhances multi-level corporate security. BI.ZONE WAF is a service that offers proactive defense against most attacks targeted at web applications and their users. BI.ZONE DFIR minimizes financial and reputational losses through prompt incident response and thorough investigation by BI.ZONE specialists.