A systematic anti-fraud loop:

Unified risk profile from all sources

Zentavor AI Anti-fraud is a self-learning system that analyzes data and user behavior to proactively counter fraud by customers, suppliers and employees. It focuses on detecting anomalies and coordinated (“paid”) actions in reviews/ratings, as well as purchase-related fraud: returns, delivery abuse, bonus abuse and more

Anomalies and patterns of user behavior

What you get

From collecting signals (reviews, ratings, purchases, returns, bonuses, delivery) and feature engineering to anomaly detection, risk scoring and controlled actions (publish/block/manual review). The solution covers 100% of the moderation flow and continuously learns from feedback

Risk scoring and real-time decisioning

detection of coordinated (“paid”) reviews

>86%

coverage of the moderation flow

100%

manual review quality (baseline)

45–60%

share of fraud detected automatically

>90%

Key business benefits:

Describe your fraud scenarios—we’ll propose an architecture, pilot hypotheses and an implementation plan with impact measurement

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AI Anti-fraud System

Unified risk profile

reviews/ratings, purchases, returns, bonuses, delivery, profiles

Data preparation & feature engineering

normalization, validations, identity resolution (phone/email/cards)

Anomaly detection

unnatural patterns, spikes, “rings”, and links (graph analysis)

ML/GenAI detection

detection of coordinated reviews, spam, and suspicious text/behavior

Risk scoring & decisioning

publish/hide, block, send to manual review

BI & investigations

dashboards, alerts, data marts for the risk team, SLA monitoring

Zentavor builds an AI anti-fraud system to proactively detect fraud by customers, suppliers, and employees based on data and user behavior

The focus is honest ratings and reviewsas well as purchase-related fraud scenarios: returns, delivery abuse, bonus abuse, and others. The system detects anomalies and flags coordinated actions for moderation and investigations

Core principle— flexibility and continuous adaptationmodels are updated regularly to keep up with new fraud schemes, while quality is controlled via metrics and business feedback

What the AI anti-fraud system includes

We automatically detect anomalies and unnatural patterns in ratings and texts to keep reviews honest and maintain trust in the rating

Coordinated reviews and rating manipulation

We define features and weights for different fraud types—returns, excessive bonus use, suspicious purchases/delivery, etc.—while minimizing false positives

Purchase-related fraud: returns, delivery abuse, bonus abuse

GenAI processes the full flow consistently, highlighting risk cases for moderators and security teams (no missed items)

Manual moderation lacks quality and coverage

We use an ensemble of methods (k-NN, Isolation Forest, autoencoders, etc.) and regular model updates so the system adapts to new fraud patterns

Fraud schemes evolve faster than rules

A predictable process from discovery to production operations

Data collection & preparation

We collect reviews/ratings, purchase history, profiles, bonuses, phone/email validations, etc.; prepare datasets and features

Anomaly & pattern detection

We detect unnatural sequences and links using an ensemble of methods (k-NN, Isolation Forest, autoencoders, etc.)

Model training & scoring

We train models for fraud types (rating manipulation, returns, bonus abuse, etc.) and configure risk scoring and thresholds

Integration into moderation & processes

Integration into moderation & processes

Model updates & quality control

We update models regularly, track drift, metrics, and decision quality, adapting to new fraud schemes

Data marts & interfaces

We design data marts and dashboards to support decisions: right granularity, consistent KPI definitions, clear visualization

Dashboards, reports & alerts

We create and validate dashboards, set refresh schedules, automated reports, and alerts for anomalies and risks

Support & improvement

We monitor pipelines and BI performance, update models, improve data quality, and ensure continuous evolution

Banking & fintech

Payment fraud and AML scenarios

−20–50%

Reduce direct losses and suspicious operations via real-time scoring, rules, alerts, and investigations with transparent KPIs

Retail & e-commerce

Orders, returns, bonus abuse

−15–40%

Reduce fraudulent orders, returns, and chargebacks while controlling false positives and impact on conversion

Marketplaces & services

Account fraud and abuse

2–6 weeks

Fast impact via phased rollout: from rules and baseline scoring to hybrid ML models and decision orchestration

Teams and domains where loss reduction and the security–conversion balance matter

Note: impact depends on data maturity, number of channels, identity resolution quality, and selected rollout scenarios

We unify transactions, events, and device signals into a single risk profile—the foundation for scoring and investigations

Unified risk profile

We detect anomalies, links, and patterns (including graph links) and manage rules/models more precisely than off-the-shelf anti-fraud systems

Anomalies and graph links

Unified response strategies: hide/publish, limit bonuses, request verification, escalate to security—based on risk and context

Decisioning & action orchestration

Built-in impact measurement: control groups, A/B tests, and KPI monitoring in BI—so you manage outcomes, not gut feelings

Measurable ROI

Describe your case: transaction fraud, returns, bonus abuse, account fraud, phishing, or AML scenarios. We’ll propose the target architecture, pilot scenarios, and an implementation plan with impact measurement

Email: info@zentavor.ai

Share your objectives—we’ll propose the anti-fraud solution and an execution plan

What we need to start

Key domains: payments, risk management, security, product, support

Data sources: transactions, web/app events, device fingerprint, KYC/profiles, lists/rules, support logs

Key KPI conflicts and bottlenecks in reporting/analytics

Refresh requirements (real-time/batch) and acceptable data latency

Access model and security: roles, audit, compliance, storage and encryption