The only fraud detection platform that identifies novel attack patterns, coordinated fraud rings, and synthetic identity schemes — without rules, without labeled data, before a single loss occurs.
Rules catch known fraud. Supervised ML catches variations of known fraud. Neither can see what’s never happened before — and that’s exactly where your biggest losses are hiding.
Trusted by leading banks, fintechs, insurance companies, and credit unions worldwide
See how 5 financial institutions stopped fraud they couldn’t see before — free download, no sales call required.
DataVisor’s unsupervised machine learning analyzes billions of events in real time to detect novel fraud patterns with no historical labels required.
You’ve invested in rules. You’ve layered on supervised machine learning models. Your team writes new detection logic every time a novel attack slips through.
And yet:
The problem isn’t your team. The problem is the fundamental approach.
Rules-based systems are reactive by definition — they can only catch what you’ve already seen. Supervised ML is better, but it still needs labeled training data. That means it learns from past fraud.
It cannot detect coordinated attacks, synthetic identity rings, or novel schemes that have no historical precedent.
Your current tools are fighting today’s fraud with yesterday’s playbook.
This is the fundamental difference with unsupervised machine learning.
“Does this transaction look like fraud we’ve seen before?”
Dependent on historical labels. Learns only from past fraud. Blind to novel schemes, synthetic identity rings, and coordinated attacks.
“Does this pattern of behavior look like it belongs — or is something anomalous happening across thousands of events right now?”
No labels required. Analyzes event relationships in real time. Detects fraud before a single loss occurs — including attacks that have never been seen before.
No labels. No training data from past fraud. UML identifies anomalous behavior by analyzing event relationships at massive scale — catching attacks that have literally never been seen before.
Individual transactions may look legitimate. UML connects the dots across thousands of seemingly unrelated events to expose rings operating in concert — the kind of coordinated fraud that passes right through rules and supervised models.
No rule-writing. No model retraining. As attack patterns evolve, UML continuously recalibrates — so your defenses evolve with the threat landscape, not months behind it.
DataVisor runs in live production environments processing some of the highest transaction volumes in financial services.
30B+
Events processed annually
15,000+
Queries per second (live)
<1ms
Real-time scoring latency
Cloud-native
Architecture — no on-prem
“DataVisor’s ability to give responses in milliseconds has helped us stop bad actor activity almost immediately.”
“Partnering with DataVisor has enabled us to create a centralized intelligence platform that has greatly enhanced our fraud detection capabilities and operational efficiency, allowing us to focus more on our members’ needs.”
“DataVisor has demonstrated an unwavering commitment to our enterprise success by seamlessly integrating their risk detection scores into our online decisioning.”
The market is flooded with platforms claiming AI-powered fraud detection. But the approach behind the AI determines whether you catch novel threats — or just variations of old ones.
You define the rules. The system follows them. If fraud doesn't match an existing rule, it passes through undetected. Every new attack requires manual rule creation. False positive rates are high because rules are blunt instruments.
Models are trained on labeled datasets of known fraud. Better than rules at catching variations, but fundamentally constrained by the same limitation: they learn from the past. They cannot detect fraud that hasn't occurred before. They require constant retraining as attack patterns shift.
DataVisor
No labels required. No historical fraud data needed. UML analyzes the full event stream in real time, detecting anomalous patterns and coordinated behaviors that have never been seen before. It's a fundamentally different approach — not an incremental improvement.
The question isn’t whether AI is part of your fraud stack. It’s which kind of AI — and whether it can actually see what’s coming next.
Five organizations. Five different fraud challenges. One approach that caught what their existing tools couldn’t.
How a fintech stopped a coordinated fraud ring operating across 3,000+ accounts
3,000+
accounts
How a credit union reduced false positives by 60% while catching more real fraud
60%
fewer false positives
How a payments platform achieved millisecond-level detection at 15,000+ QPS
15,000+
QPS
Real metrics: detection rates, false positive reduction, time to deployment
One email. No sales call, no 12-email nurture sequence.
Three questions that come up before every enterprise deployment — answered with what actually happens, not what sounds good in a deck.
DataVisor deploys in weeks, not quarters. The platform integrates via flexible APIs and supports SaaS deployment — no on-premise hardware, no 18-month implementation timelines.
“DataVisor enables us to deploy strategies in just 15 minutes.”
DataVisor is designed to augment, not replace. It layers into your existing fraud infrastructure via REST APIs and supports real-time and batch processing. The platform works alongside your current rules engine and supervised models — adding the UML detection layer that catches what they miss.
“DataVisor's feature store and their data ingestion platform are second to none.”
Legacy systems generate noise. UML generates signal. By analyzing event relationships rather than individual transactions against static thresholds, DataVisor dramatically reduces false positives — meaning your investigators spend their time on real threats, not phantom alerts.
60%
reduction in false positives reported by banks using DataVisor — while simultaneously catching more actual fraud.
Forrester Wave Leader
AML Solutions · 2025
G2
Users Love Us
Datos Insights Impact Awards
Fraud Winner · 2025
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Console view — alert triage queue
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Time-to-production from API key to live scoring
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Drill-down on detected anomaly cluster
Right now, novel fraud patterns are moving through financial systems — patterns that rules can’t catch, supervised ML can’t learn from, and your investigators can’t see.
Unsupervised machine learning was built for exactly this moment. See how five financial institutions used DataVisor’s UML to catch fraud that their existing tools missed — and what that meant for their bottom line.
Instant access. Just your work email. See the results in under 5 minutes.