DataVisor, the leading fraud detection company with solutions powered by transformational AI technology, announced today the availability of its Feature Platform. This best-in-class solution automates the feature engineering process by producing thousands of auto-derived features based on user-imported raw data and mapped fields.
Feature engineering is a historically complex and time-consuming process with each new feature requiring multiple steps to complete. Creating high-quality features can, therefore, be extremely tedious. Considering that large organizations need a significant number of these features to successfully address different business issues, the resources required can be significant.
Automated Feature Engineering and Feature Recommendation
With the introduction of the DataVisor Feature Platform, organizations now have access to automated feature engineering across multiple data sources that empowers data science and business teams to build powerful features in minutes, instead of weeks or months.
Furthermore, the DataVisor solution can recommend features that are already optimized for specific use cases, particularly to address risk and fraud issues. For example, if the organization focuses on transaction fraud, Feature Platform will recommend a list of features that are readily available and uniquely important to that scenario to deliver strong detection results immediately.
Effortlessly Create Complex Custom Features and Centralize Maintenance
A core component of the DataVisor Feature Platform is the flexibility it delivers to organizations. Not only does it deliver automated feature engineering capabilities, but it also offers the ability to custom engineer features that are specific to the organizational data and needs.
Teams can engineer any features using the comprehensive functions and operators built into the Feature Platform. The user-friendly environment enables the creation of these powerful features with just a few clicks or via simple coding, all through a UI, with no additional support from IT departments necessary, and turn those on with the same fidelity into production right away.
Because these features are built in a centralized platform, it enables re-use and easy maintenance, to allow multiple different teams to share features instead of starting from scratch for different business tasks.
Advanced Deep Learning Features
The feature engineering platform supports advanced features derived through deep learning technologies such as suspicious patterns from user-generated content. Organizations do not need investing in separate infrastructure and technology stack to benefit from deep learning technologies.
Global Intelligence Network Features
With the DataVisor Feature Platform integrating with its Global Intelligence Network (GIN), feature derivation and model performance are further improved.
The DataVisor GIN is powered by derived signals from more than 4.2 billion protected accounts and in excess of 800 billion events across industries. This significantly enhances machine learning with fine-grained digital intelligence that encompass everything from IP address patterns to user agent strings and more.
“The hallmark of the DataVisor approach is our holistic approach to data analysis using sophisticated feature engineering and machine learning. The Feature Platform incorporates this ethos and delivers organizations with proven means to develop effective machine learning models more easily and faster, and hence stop fraud loss from occurring,“ said DataVisor CEO Yinglian Xie.
To request a demo go to https://www.datavisor.com/products/feature_platform/.
DataVisor is the leading fraud detection platform powered by transformational AI technology. Using proprietary unsupervised machine learning algorithms, DataVisor restores trust in digital commerce by enabling organizations to proactively detect and action fast-evolving fraud patterns, and prevent future attacks before they happen. Combining advanced analytics and an intelligence network of more than 4B global user accounts, DataVisor protects against financial and reputational damage across a variety of industries, including financial services, marketplaces, ecommerce, and social platforms.