DataVisor Enterprise Fraud and Risk Platform

DataVisor platform integrates different signals, third-party data, and heterogeneous data sources to deliver superior fraud detection and minimize financial loss. Powered by an extensive array of tools and machine learning approaches, that include Unsupervised and Supervised learning, rules engine, feature engineering, device intelligence and link analysis, our platform enables a holistic fraud prevention strategy for enterprises to combat the speed, scale, and sophistication of modern fraud.

Manage Complexity with Agility

Product Tiers

Add-On Modules

Insights Center

Analyze production data to monitor risk profile changes and define new strategies. Enable fraud analysts to process real-time data without IT dependency. Inform business leaders with the latest risk analytics and empower fraud strategists to optimize fraud defense with guided analysis and validation steps.

Knowledge Graph

Ingest internal and third-party data in real time to perform entity resolution and linkage analysis. Auto discover and prioritize the most suspicious patterns to save the operation team’s time. Provide smart investigation that drills deep to get granular insights. Support directly adding entities to blocklists and allowlists from UI.

Device and Behavior Intelligence SDK

Detect fraud by using device signals, user behavior data and risk indicators. Uncover tactics targeting mobile devices, including emulators, botnets, rooting, hooking, cloud phones and more. Consistently generate unique device IDs, no matter how fraudsters might manipulate the apps or devices.

Open SaaS Fraud Platform. Rapid Time to Value.

DataVisor’s solution helps organizations have complete control of your fraud management workflow to proactively manage fast-evolving fraud and create frictionless customer experience. It scales infinitely, provides customizable plug-and-play modules and supports rapid and flexible integration with your existing systems, orchestration solutions and third-party vendors in two weeks.