DataVisor is an AI-powered fraud and AML platform for banks and enterprises. See 2026 pricing model, key features, pros, cons and FAQs.
Category
Automation
Pricing
Custom enterprise subscription, priced by data volume, protected accounts, and deployed modules, from Pricing available on request; no published flat starting price
Verified
Not yet
Last updated
July 19, 2026
Founded
2013
Headquarters
Mountain View, California, United States (with offices in Beijing and Shanghai, China)
Unsupervised Machine Learning for Fraud Detection
DataVisor built its early reputation on unsupervised machine learning, an approach that looks for coordinated, suspicious patterns across large numbers of accounts and events without requiring pre-labeled fraud examples, which helps it catch new attack patterns rather than only fraud types a supervised model has already seen.
The platform is built for high throughput and low latency, reportedly capable of processing more than 15,000 queries per second while computing risk features in under 100 milliseconds, which matters for real-time decisions like transaction authorization or login screening.
Platform Modules: Rules, Decisioning, and AI Agents
The platform includes a Feature Platform for real-time feature computation, a visual Rules Engine that lets analysts build and tune rules without engineering support, and a Decision Flow module for constructing multi-step fraud decisioning strategies through a drag-and-drop interface.
DataVisor has expanded into conversational AI agents that can execute across fraud and anti-money-laundering workflows end to end, covering strategy design, rule tuning, investigation, and reporting tasks that previously required manual analyst work.
Pricing Model and Best Fit
DataVisor uses custom, subscription-based enterprise pricing driven by data volume, the number of protected accounts, and which fraud and anti-money-laundering modules an organization deploys, with quotes provided directly by DataVisor's sales team.
The platform is oriented toward mid-size and large financial institutions, payment companies, and other enterprises with meaningful fraud exposure and dedicated risk teams, rather than very small businesses or individual users.
Key Features
Unsupervised machine learning detection — Identifies coordinated, suspicious patterns across accounts without needing pre-labeled fraud examples.
Real-time account takeover detection — Correlates unusual logins, device changes, and behavioral signals to catch account takeover as it happens.
Onboarding fraud detection — Verifies new customers during onboarding while blocking synthetic identities and stolen-identity applications.
Card-testing and payment fraud detection — Identifies counterfeit payment instruments and card-testing attacks within milliseconds of a transaction.
High-throughput real-time feature computation — Processes over 15,000 queries per second and computes risk features in under 100 milliseconds.
Visual Rules Engine — Lets fraud and risk analysts create and tune detection rules without engineering support.
Drag-and-drop Decision Flow — Builds multi-step fraud decisioning strategies visually, such as routing transactions through extra verification.
Conversational AI agents — Execute across fraud and anti-money-laundering workflows, from strategy design to investigation and reporting.
Pros & Cons
Pros
Unsupervised learning catches novel fraud patterns rules alone miss
Very high claimed throughput and low decision latency
Combines rules, machine learning, and AI agents in one platform
Modular design covers onboarding, account takeover, payments, and AML
Established track record with financial institutions since 2013
Cons
Pricing is not published and requires a sales conversation
Enterprise focus makes it a poor fit for very small businesses
Implementation and tuning likely require dedicated risk or data expertise
Custom pricing makes cost comparison against competitors difficult upfront
Full capabilities depend on which modules an organization purchases
Frequently Asked Questions
What does DataVisor do?
DataVisor is an AI-powered fraud and risk management platform that detects fraud, account takeover, and money laundering in real time for banks and enterprises.
Who uses DataVisor?
DataVisor is used by financial institutions, fintech companies, payment providers, and other enterprises with meaningful fraud exposure.
How does DataVisor detect fraud without labeled data?
DataVisor uses unsupervised machine learning to find coordinated, suspicious patterns across accounts and events without needing prior labeled fraud examples.
Does DataVisor handle anti-money-laundering as well as fraud?
Yes, DataVisor's platform includes AML capabilities alongside fraud detection, including AI agents for AML case workflows.
How much does DataVisor cost?
DataVisor uses custom enterprise pricing based on data volume, protected accounts, and deployed modules; pricing is available on request.
Where is DataVisor headquartered?
DataVisor is headquartered in Mountain View, California, with additional offices in Beijing and Shanghai, China.
When was DataVisor founded?
DataVisor was founded in 2013.
Does DataVisor use AI agents?
Yes, DataVisor has introduced conversational AI agents that execute across fraud and anti-money-laundering workflows end to end.