Anti-fraud ML model
We implement an anti-fraud ML model: the system detects suspicious transactions, orders and actions (fraud, abuse, manipulation) by behavior patterns — to reduce losses. Honestly upfront: anti-fraud is a probabilistic model, not 100% protection: it produces both false positives (blocking honest clients) and misses (not catching some fraudsters); it requires constant monitoring and retraining because fraudsters adapt; and it complements rather than replaces other security measures and common sense.
Anti-fraud ML model — overview
An anti-fraud ML model is a system that assesses the risk of operations (transactions, orders, registrations, actions) based on patterns: unusual behavior, anomalies, signs of automation/manipulation, atypical amounts/frequencies — and flags or blocks the suspicious. We tune it for your data and risk scenarios. Honestly about the probabilistic nature, this is key: anti-fraud does NOT give 100% protection. It is always a balance of two errors: false positives (blocked an honest client, lost a sale and trust) and misses (false negatives — did not catch a fraudster, incurred losses). Making 'zero of both' is impossible — you can only tune the balance to your risk tolerance. Promising 'we will catch all fraud and never touch honest ones' is dishonest. Honestly about fraudster adaptation: fraud is a race. Fraudsters change schemes, so the model must be constantly monitored and retrained; 'set and forget' does not work — effectiveness drops over time without support. Honestly about data: quality depends on your historical data about good and fraudulent operations; at the start with little data accuracy is lower, growing as it accumulates. Honestly about the human's role: disputed cases must be decided by a human (manual review of flagged ones), not blind automatic blocking — otherwise false positives hit honest clients. Honestly about 'complements, not replaces': ML anti-fraud is part of protection, not all of security; basic measures (validation, limits, 3DS etc.) are also needed. Honestly about the effect: it reduces fraud losses but not to zero and not without support. Honestly about access: data on operations and fraud labels is needed. An important boundary: this is anti-fraud; predictive scoring (LTV/churn etc.) — 882; general site security — a separate section. Picture this: instead of 'fraud losses and manual catching' — a system that flags risk, with an error balance tuned to you and human review. The base price starts from 70,000 ₽ (depends on data and scenarios).
Problems we solve
- Losses from fraudulent orders/transactions/manipulation.
- Tracking fraud at scale by hand is impossible.
- Simple rules are easily bypassed, and honest ones are sometimes blocked.
- No understanding of which operations are really suspicious.
What's included in the Anti-fraud ML model service
- ML risk assessment of operations by behavior patterns
- Flagging/blocking the suspicious for your scenarios
- Tuning the balance of false positives and misses to your tolerance
- Human manual review of disputed cases
- Monitoring and retraining (fraudsters adapt)
- Honest boundaries (not 100%; complements, not replaces security)
- Reliance on your data (accuracy grows with data)
- Handover and review with you
What you get
- Reduced fraud losses (not to zero — honestly)
- The suspicious is flagged for review
- The error balance is tuned to your risk tolerance
- A monitoring and retraining process (not 'set and forget')
How the work goes: steps
- We collect operation data and fraud labels; clarify scenarios
- We train/tune the model, set the error balance
- We deploy with human review, set up monitoring and retraining with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Will anti-fraud catch all fraud?
No, and that is honest: it is a probabilistic model, not 100% protection. There is always a balance of two errors — false positives (blocking an honest client) and misses (an uncaught fraudster). Reducing both to zero is impossible, you can only tune the balance to your risk tolerance. Promising 'we will catch everything and not touch honest ones' would be deception.
Is it enough to set it up once?
No — fraud is a race. Fraudsters change schemes, so the model must be monitored and retrained; without support effectiveness drops over time. Also disputed cases must be reviewed by a human, not blind auto-blocking. 'Set and forget' does not work in anti-fraud, and we honestly build in support rather than a one-off installation.
Will this replace the rest of fraud protection?
No, honestly: ML anti-fraud complements rather than replaces security. Basic measures (validation, limits, payment checks like 3DS, monitoring) are also needed. Anti-fraud is a powerful layer based on behavior patterns, but alone it does not cover everything. We embed it into overall protection rather than pass it off as the only and sufficient solution.
About the provider
The «Anti-fraud ML model» service is provided by PDV Expert — a team specialising in «Site quality». We work under contract and deliver a written report with recommendations.