Ensure safe and rapid business growth, improve profitability, and minimize customer friction with DataVisor’s complete platform that eliminates legacy systems, removes vulnerabilities, and improves efficiencies. Fraud leaders benefit from a single, comprehensive platform that supports multiple uses, ensures full compliance, and improves governance.
Devise new defense strategies, monitor fast-evolving fraud trends and run analysis with the full fidelity of production data. Fast, automated SaaS onboarding allows you to go live in weeks instead of months. Fraud strategists benefit by being able to develop and optimize strategies with guided analysis and validation steps, and deploy new strategies without the need for tech team help.
Boost operational efficiency with auto decisions, bulk actions and accelerated investigations. Leverage an integrated AI approach that combines machine learning and rules for maximum detection and full transparency and explainability. Fraud operations teams benefit from global, intelligent search and link analysis at multiple levels to uncover hidden patterns and empower contextual decisions.
Protect transactions as they occur
Scales to support the largest fraud and risk management systems in the world. With a peak traffic rate of over 15,000 QPS, DataVisor boasts a hyper-low end-to-end detection latency of under 100ms while supporting sophisticated velocity features, rules, and machine learning model computation. DataVisor empowers organizations to make informed real-time fraud decisions with high confidence, especially for use cases such as real-time payments.
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Make informed fraud decisions
Empowers fraud teams and business users to derive actionable insights from holistic data analysis by orchestrating numerous data sources. Integrations with third-party data providers enrich fraud signals with additional data inputs, increasing detection accuracy and speed. With DataVisor’s contextual data orchestration, fraud teams are able to improve customer experience and realize cost savings by only authenticating users when needed instead of making calls for identity verification for every user.
Uncover new fraud patterns proactively
Leverages both supervised and unsupervised machine learning to provide the best fraud detection performance. SML enables organizations to uncover fraud that follows characteristics of fraudulent behavior that are well understood, while UML is essential for detecting unknown and emerging patterns of fraud that may not have been previously identified. When used in combination, SML and UML provide the most comprehensive approach to detecting criminal behavior.
Accelerate fraud detection response times
With the most powerful real-time feature platform, fraud professionals and business users alike can derive valuable signals across multiple levels of entity relationships across large volumes of data points easily, often without the need for any coding. Pre-built feature packages are optimized for specific fraud types and scenarios and can reduce the time spent manually building features from scratch for rules and ML models, accelerating the response to new fraud attacks.
Enhance manual reviews
Enables fraud analysts and investigators to visualize linkages among connected fraud incidents while making bulk decisions with higher accuracy, significantly increasing operational efficiency. It supports embedding third-party web pages, internal UIs, or Maps, so analysts can gather all information in one place for fast investigation. Additionally, intelligent search and graph analysis at multiple levels can enable deeper investigations.
Build more effective strategies
Provides a wide array of information that assists with building, evaluating and extending fraud strategies. It supports using production data in a powerful analytical database to allow fine-grained evaluation of false positives/negatives, supporting guided analyses that inform new strategies.
Automatic model retraining and rule tuning also reduce labor and costs while minimizing human error, and the quick deployment of new, more effective models maximizes ROI.
Identify not only accurate device IDs, but also sophisticated attack techniques such as emulators, botnets, rooted and hooked devices. It additionally captures a wide variety of behavior intelligence signals such as copy and paste, and typing speed.
Leverage deep link analysis to identify and visualize connections between individuals, organizations, networks activities and more, to accelerate contextual decision-making, accelerate manual case reviews, and detect more fraud with fewer false positives.
Build unsupervised machine learning models with an open platform to detect unknown fraud without waiting for historical data and labels. Have complete control when building models, get full transparency and explainability, and meet compliance requirements.
Reduce financial losses and manual review costs with accurate detection results.
Boost review and decision with link analysis, smart investigations, auto decisions and bulk actions.
Provide flexible integration with your systems and support real time and batch processing.