--- title: AI Anti-fraud — Real-time Fraud Detection | Zentavor description: Real-time risk scoring that catches fraud while clearing legitimate customers: explainable, adaptive, and far fewer false alarms. canonical: https://zentavor.com/solution-anti-fraud.html --- # AI Anti-fraud — Real-time Fraud Detection | Zentavor > Real-time risk scoring that catches fraud while clearing legitimate customers: explainable, adaptive, and far fewer false alarms. Solutions / AI Anti-fraud Catch fraud in real time. Clear good customers Real-time risk scoring that stops fraud at the decision point, while letting legitimate customers through untouched Real-timeExplainableAdaptive 9:41 Fraud Operationsreal-time risk scoringOn-premise Fintech / Banking · in production 0.18 Approved cleared in 92 ms · threshold 0.70 tx 0xA82F · low-risk · device known Recent decisions Aurora Retail$2480.12 Unverified merchant$9,8000.94 real-time scoring1.2M / day −94%false-positive rate <120 msin the payment path 24/7real-time, in production Inside the decision ## It doesn't just block. It shows why Every transaction is scored against real fraud signals. Watch a few real patterns get caught, with the reason codes that satisfy compliance Scored today 1,286,676 Blocked 331 False positives −94% vs last 7 days p90 latency 118 ms vs last 7 days TimeMerchantAmountScoreStatus Case #48326REVIEW $138.45 · Zara new merchant · amount above average Why flagged 0.67risk · threshold 0.70 AI summary · copilot A couple of mild anomalies but low overall risk, held for review rather than declined. BlockApproveEscalate Graph intelligence ## Catch the ring, not just the transaction A rules engine sees four clean payments: each under $10k, each on a normal-looking account. We score the connections and see one money-laundering ring. That's the kind of organized fraud single-transaction systems miss Detected ring Structuring · 4 accounts · $38k ACC-4471$9,800London · shared device d8f1·· ACC-7758$9,400Moscow · shared IP ACC-1130$9,600Dubai · shared device d8f1·· MULE-002$38k inHong Kong · fan-in · cash-out Individually, each payment clears. Connected, they form one laundering ring, and it gets caught. From signal to decision ## A system that adapts, not a static rulebook Streaming features feed an ensemble, a calibrated score drives the policy, and analyst feedback retrains the model on new patterns run #8842 · transaction scoredcompleted · 118 ms end-to-end streaming features velocity · device · geo · graph ensemble model gradient-boosted + sequence calibrated score probability + reason codes decision policy approve · step-up · block analyst feedback labels & case review retrain nightly · tracks new patterns The challenge ## Rules that block your best customers Legacy rules engine Legacy rules block good customers along with the bad. Sub-threshold payments slip through as organized rings. Opaque allow/deny decisions fail compliance. New fraud patterns need analysts to hand-write rules. With Zentavor Real-time risk scoring clears legitimate customers, reducing false positives by 94%. Graph intelligence catches the ring, not just the transaction (e.g. a $38k structuring ring). Reason codes + full audit trail on every decision. Models retrain nightly and adapt. Scored in <120 ms in the payment path. FAQ ## Your frequently asked questions ** Can it score in the payment path without adding latency?** Yes. Scoring runs on precomputed streaming features, returning a decision in well under 200 ms, inside a checkout latency budget. ** Is the model explainable enough for compliance?** Every score ships with reason codes and a full audit trail, so compliance can see exactly why a transaction was approved, stepped up or blocked. ** How does it keep up with new fraud patterns?** Analyst labels feed a retraining loop, so the model tracks emerging patterns instead of waiting for someone to write a new rule. ** Does our data leave our environment?** No. The system runs inside your perimeter, on-premise or private cloud, so transaction data never leaves your environment. Let's talk ## Stop fraud without stopping revenue Share your transaction profile and risk constraints. We'll scope a model and integration plan Request a demo