YooMoney's YuScan Automates E-commerce Risk Assessment Scanning Up to 1,000 Sites Per Hour
YooMoney has published a detailed technical overview of YuScan, its internal service for automated risk assessment of e-commerce websites. The tool helps banks and payment organizations detect merchants that attempt to mask prohibited or high-risk activities behind seemingly legitimate storefronts.
Since its launch in 2020, YuScan has analyzed more than 550,000 merchant applications. During this period YooKassa has not received any regulatory fines related to servicing prohibited business activities.
The service is intended for organizations that process over 10,000 pages daily, work with acquiring, marketplaces, KYC/AML checks, or must comply with Russian Federal Law 152-FZ. Manual analysis of such volumes quickly becomes a bottleneck, so YuScan was developed to perform deep, automated audits at scale.
From URL to Report in Minutes
YuScan starts from a merchant’s homepage and recursively crawls the entire site structure. It behaves like a real browser by executing JavaScript, waiting for dynamic elements, and scrolling through pages. This approach reveals content that would remain hidden under simple HTTP requests.
Asynchronous processing allows the system to handle up to 1,000 sites per hour. For modern sites that rely heavily on JavaScript, the crawler uses the Playwright library. When standard automation is blocked by advanced anti-bot systems such as Cloudflare, YuScan switches to Camoufox, which modifies browser behavior at the C/C++ level to produce cleaner fingerprints and avoid detection.
Technology Stack and Architecture
The service is written in Python. The API layer is built with FastAPI, data is stored in PostgreSQL, and the crawling core is based on the open-source Scrapy framework. Playwright handles browser automation while custom scaling and risk-scoring logic sit on top of Scrapy’s queue and pipeline system.
After data collection, multiple analysis stages begin. Text, images, reviews, external links, and registration details are examined. Images are converted into vector embeddings and compared against sensitive categories such as alcohol, tobacco, and online gambling. Large language models evaluate context to reduce false positives.
External Signals and Final Scoring
YuScan also checks whether a domain appears in Roskomnadzor registries, reviews third-party feedback, analyzes WHOIS records, and looks for signs of cloned or fraudulent sites. Extracted company details (INN, KPP, OGRN, contacts, and legal documents) help distinguish real businesses from temporary or opaque operations.
Since the introduction of automated checks, merchant onboarding times have improved significantly: simple cases are completed in under three hours, half of all companies begin processing payments within one day, and seven out of ten finish the process within two days.
Related articles
Students Recruited as Money Mules: How Scammers Target Youth with Fake Jobs, Gaming Lures and Sextortion
Russian students and schoolchildren are increasingly targeted by fraudsters offering easy part-time work, free in-game currency, or urgent academic verifications that lead to money mule schemes or account takeovers. Criminals exploit young people's need for income, interest in games like Roblox, and lack of experience with financial verification processes. Common tactics include fake job postings that turn participants into droppers under Article 187 of the Russian Criminal Code, phishing calls impersonating university staff, and malicious game mods for Minecraft or Brawl Stars. Victims risk criminal liability, banking restrictions from the Bank of Russia, and sextortion using AI-generated images. The report details seven major scam types and provides concrete protection steps including two-factor authentication and the Take It Down service.
OTP Bank Details How It Detects Professional Cashback Hunters and Card Rental Schemes
OTP Bank’s head of information security control, Alexander Shirokov, explains where legitimate use of cashback programs ends and abuse begins. The bank distinguishes rational use of tariffs from deliberate schemes created solely to extract maximum bonuses. Professional cashbackers who study rules in detail, rent cards, or share privileges are monitored through behavioral analytics and ML models. Such activity creates concentrated load on loyalty programs and can force banks to adjust conditions. The institution stresses that automatic systems are only the first layer and analysts review borderline cases to avoid penalizing ordinary customers.
Russian Li Auto Owners Face Master Account Theft and Extortion Up to 45,000 Rubles
Russian owners of Li Auto vehicles are experiencing a wave of master account compromises that allow attackers to seize control through the official mobile app. After losing access, some vehicles lose screen functionality, climate control, and in certain cases the ability to start. Attackers then contact victims demanding 35,000 to 45,000 rubles for account return. Approximately 20 confirmed cases have been reported, though the real number may be higher. The exact infection vector remains unknown, and even owners who previously used the paid eSender service to retain Chinese phone numbers are now affected. Li Auto and official distributor Sinomach Auto are developing recovery procedures and plan to migrate account binding from Chinese numbers to email addresses.
How Vacation Photos Enable Family Impersonation Scams and Data Exposure
Public photographs from vacations and family events can reveal names, relationships, addresses, travel dates, and even security question answers without any hacking required. Attackers combine airport shots, hotel tags, school certificates, and relative comments to build convincing stories for emergency scams targeting grandparents or parents. The FTC has documented how scammers impersonate relatives using real details pulled directly from social media. NIST explicitly discourages security questions based on easily discoverable personal facts such as pet names or school names. Metadata like EXIF GPS coordinates and visible documents in the background further increase exposure risks. Practical steps include reviewing backgrounds, removing metadata, limiting audience settings, and establishing family code words that do not appear in public posts. The guidance covers iPhone, Android, Windows, and macOS methods for stripping location data before sharing images.