Fraud
Zero
Zero
{ Services }
From audit and vulnerability analysis to building anti-fraud architecture and monitoring
We deliver a full cycle of anti-fraud solutions and tools
We create dashboards based on key anti-fraud metrics and their correlation with business KPIs. We configure alerts and integrations with BI systems.
Visualization and monitoring setup
Deep dive into the company’s operations, analysis of existing protection mechanisms, identification of vulnerabilities and blind spots. Preparation of recommendations to improve detection logic and preventive measures.
Initial audit of anti-fraud processes
We deploy ML models for real-time assessment and activity detection on the platform: user action scoring, behavior classification, anomaly detection.
Implementation of machine learning models
We identify and filter out illegitimate traffic to ensure accurate analytics and a clean dynamic of key metrics.
Analysis of incentivized and anomalous traffic sources
{ Services }
From audit and vulnerability analysis to building anti-fraud architecture and monitoring
We deliver a full cycle of anti-fraud solutions and tools
We create dashboards based on key anti-fraud metrics and their correlation with business KPIs. We configure alerts and integrations with BI systems.
Visualization and monitoring setup
Deep dive into the company’s operations, analysis of existing protection mechanisms, identification of vulnerabilities and blind spots. Preparation of recommendations to improve detection logic and preventive measures.
Initial audit of anti-fraud processes
We deploy ML models for real-time assessment and activity detection on the platform: user action scoring, behavior classification, anomaly detection.
Implementation of machine learning models
We identify and filter out illegitimate traffic to ensure accurate analytics and a clean dynamic of key metrics.
Analysis of incentivized and anomalous traffic sources
{ Cases }
The founder worked in anti-fraud development and analytics in major CIS digital companies, engaged in building ML models and anti-fraud algorithms. This hands-on experience formed the foundation of preventive fraud protection systems.
Expertise based on real anti-fraud practice
Case 1: HR platform (top-3 globally)
{ Result }
The vulnerability was completely eliminated, the scheme blocked.
Daily losses amounting to millions of rubles were prevented.
{ Task }
Identify the bypass scheme, determine abuse patterns, and close the vulnerability.
{ Solution }
Analysis of technical and behavioral patterns: combinations of IP, user-agent, fingerprints, device_id.
Development of heuristic sets, velocity checks, and honeypot tests.
Creation of a combined monitoring dashboard and alert system.
Enhancement of detection models.
{ Problem }
Through the platform, it was possible to access candidate data without purchasing the service.
One company searched for rare resumes and passed them to another company, which contacted the candidates directly.
Case 2: Employer rating platform
{ Result }
25% of suspicious votes removed. 8% of bots blocked. The rating was cleared of illegitimate activity.
{ Task }
Clean the voting from fake activity and reduce distortion of rating results.
{ Solution }
Detection of technical and behavioral patterns. A set of detection heuristics: velocity checks, honeypot tests. Monitoring dashboard and alert system.
{ Problem }
Automated activity was detected: mass voting from the same devices and networks, imitating real user behavior.
Case 3: Largest delivery service
{ Result }
Payments to fraudulent couriers were blocked. Order theft schemes were eliminated. Financial losses were significantly reduced.
{ Task }
Identify the scheme, stop fund withdrawals, and prevent further abuse.
{ Solution }
Models for real-time evaluation of courier actions.
Restrictions for new performers.
Monitoring and alerts for recurring patterns and anomalous geo-activity.
Training algorithms on real-time data.
Restrictions for new performers.
Monitoring and alerts for recurring patterns and anomalous geo-activity.
Training algorithms on real-time data.
{ Problem }
Circumvention of security mechanisms: order theft, fraudulent compensations to counterparties, and rapid cash-outs by couriers.
{ Contacts }
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