Fraud
Zero
Zero
{ Services }
We build a complete anti-fraud solution
We build dashboards, key anti-fraud metrics and setup alerts within BI systems
Monitoring setup
Comprehensive research of the company’s services, analysis of current protection mechanisms and vulnerabilities.
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 block sources of bot traffic, making the analytics and key metrics valid
Analysis of abnormal traffic
{ Services }
We build a complete anti-fraud solution
We build dashboards, key anti-fraud metrics and setup alerts within BI systems
Monitoring setup
Comprehensive research of the company’s services, analysis of current protection mechanisms and vulnerabilities.
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 block sources of bot traffic, making the analytics and key metrics valid
Analysis of abnormal traffic
{ Cases }
The founder gained his experience in anti-fraud development and analytics working for major CIS digital companies, built anti-fraud ML models and algorithms. This hands-on experience formed the core of preventive fraud protection systems.
Expertise based on hands on anti-fraud practice
Case 1: HR platform (top-3 globally)
{ Result }
The vulnerability was eliminated. Prevented up to 100000$ of daily losses.
{ Challenge }
Identify the bypass scheme, determine abuse patterns, and close the loophole.
{ Solution }
Built rules based on technical and behavioral patterns: combinations of IP, user-agent, fingerprints, device_id. Implemented heuristic sets, velocity checks, and honeypot tests. Launched monitoring and alert system. Improved the quality of detection models.
{ Problem }
Through the platform, it was possible to access candidate data without purchasing the subscription.
One company searched for rare resumes and passed them to another company, which contacted the candidates directly.
Case 2: Employer rating platform
{ Result }
The vulnerability was eliminated. Prevented up to 100000$ of daily losses.
{ Challenge }
Make the process legit and free from abnormal traffic.
{ Solution }
Implemented real time ML models based on technical and behavioral patterns. A set of detection heuristics: velocity checks, honeypot tests. Deployed monitoring system.
{ Problem }
Abnormal activity on the platform – mass bot and motivated registration and voting from the same device and network clusters, imitating real user behavior.
Case 3: Largest delivery service
{ Result }
Fraud withdrawals were blocked. Order theft schemes were eliminated. Financial losses were considerably reduced, almost by 200000$ per month
{ Challenge }
Identify the patterns, block withdrawals, and prevent further abuse.
{ Solution }
ML models for real-time evaluation of courier activity.
Built monitoring and alerts for recurring patterns and abnormal geo tracks. Deploying algorithms on real-time data.
Built monitoring and alerts for recurring patterns and abnormal geo tracks. Deploying algorithms on real-time data.
{ Problem }
Abuse of security mechanisms: order theft, instant cash-outs by couriers.
{ Contacts }
We’ll show where your company is losing money and how to stop it – We’ll show how to stop losing money and trust of your clients
Evaluate your business protection level – Make your platform safe and sound
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