Real-time risk scoring in < 50ms, AI/ML anomaly detection, configurable anti-fraud rules, continuous transaction monitoring, and automated PLD/FT regulatory reporting — all via API.
Risk scoring
Anomaly detection
False positive rate
Automated reporting
Transaction monitoring
Screening
RiskOS evaluates every API call in < 50ms. AI/ML models analyze behavioral patterns, device fingerprints, and transaction context. Rules engine enforces configurable policies, and every decision is logged for PLD/FT reporting.
Scoring
< 50ms
Rules
Configurable
Screening
PEP/Sanctions
Monitoring
Real-time
From risk scoring to regulatory reporting — every fraud prevention operation accessible via REST endpoints.
Real-time risk scoring
Score any transaction in < 50ms. AI/ML models evaluate amount, velocity, device, and behavioral patterns.
1curl https://api.revenu.com/risk/score \2-u:prod_key \3-d transaction_id='txn_7xMn8kR5' \4-d amount=150000 \5-d device_id='dev_9pQr2sT4'▌
Detection
Ensemble models trained on financial fraud patterns. Continuous learning from your transaction data.
Hardware, browser, and network fingerprinting. Detect emulators, VPNs, and device spoofing.
Baseline user behavior and detect deviations. Typing patterns, navigation flow, and session analysis.
24/7 transaction monitoring with automatic escalation. Configurable alert thresholds per risk level.
Prevention
Velocity, amount, geo, time-window, and custom conditions. Hot-deploy without code changes.
Dynamic lists by CPF, CNPJ, device, IP, or custom attribute. API-managed with instant propagation.
Automatic account/device blocking when fraud patterns are detected. Auto-unblock after review.
Trigger MFA, liveness check, or manual review based on risk score. Configurable per transaction type.
Developer
Test scoring, rules, and screening with simulated fraud patterns in production-identical environment.
False positive rates, detection metrics, rule performance, and trend analysis via API.
Events for every risk decision: scored, flagged, blocked, reviewed, and cleared.
Go, Node.js, Python, and Java. Mobile SDKs for device fingerprinting (iOS/Android).
“RiskOS reduced our fraud losses by 97% while cutting false positives from 8% to 0.08%. The AI scoring catches patterns our rule-based system never could. COAF reports are now fully automated.”
Fraud reduction
False positive rate
Scoring latency
Score transactions, test anti-fraud rules, simulate screening, and explore risk analytics — all with pre-loaded fraud patterns in a production-identical sandbox.
Test credentials in minutes.
Free sandbox environment · No credit card
Direct access to complete banking infrastructure: accounts, cards, payments and capital. Build your financial products on your terms, without unnecessary intermediaries.