3 Capabilities of Advanced Fraud Features that Help Your Fraud Team Adapt to Fast-Evolving Fraud

Features engineering enables you to create data attributes to use in a model or, in the case of DataVisor’s operational platform, integrated with a rules engine. The benefits are many: The ability to quickly identify fraud patterns, increased accuracy and lower false positives, fewer manual reviews, and better use of your team’s time and resources. But before organizations can realize these benefits, they have to overcome the challenges that have prevented effective features engineering in the past.

Historically, feature development has been a manual, time-consuming process that involved extensive testing. Data scientists would need to understand which features were relevant in the context of fraud, and have the computational infrastructure to roll them into production for real-time fraud prevention. Due to this tedious process of testing and deployment, fraud detection and prevention was reactive and ineffective.

Another major challenge has been a lack of centralization. Companies often have their own feature libraries of customized features. However, when features are created in siloes, it’s difficult to manage them across teams or derive synergies. What’s more, a lack of fraud-specific domain knowledge, computational limitations and an inability to push features into production prevent businesses from deriving the full potential value of their features.

Fortunately, DataVisor is enabling organizations to take full advantage of the powerful capabilities of fraud features. Here are three key elements of the DataVisor platform that help organizations overcome the typical challenges of features development: 

1. Pre-Packaged, Out-of-the-Box Features

The DataVisor Feature Library provides out-of-the-box feature sets by use case, as well as by tags (such as email, IP address and so on). This enables fraud teams to leverage proven fraud features to adapt to emerging fraud immediately and protect their organizations from fraud losses. 

Auto-derived features play a big role in helping companies adapt to fraud quickly and easily. However, in some cases, organizations need to create features that are unique to their business. In such situations, users can leverage the DataVisor’s basic operator functions (mathematical, string or conditional coding), as well as velocity functions and advanced user-level, event-level or cluster-level operators. DataVisor offers a thorough pre-processing environment to test features prior to deploying them into production.

Read this post to learn more about pre-packaged features and how fast and effortless it is to use them with a rules engine. 

2. An easy-to-use interface for advanced features development 

With DataVisor’s easy-to-use UI, fraud teams with absolutely no data science or model-building expertise can create advanced features that enhance fraud detection. The image below shows the interface for creating custom features for a defined time period using drag-and-drop fields and operators.

3. A codable interface for more advanced users and data scientists

For more advanced users, DataVisor’s Features Platform provides the ability to write code. Using the UI or coding environment, users can stack features to create advanced, powerful features that enhance fraud detection, without the additional complexity of rules management and maintenance. Once features are created, users can easily integrate them with any modeling platform.

Tap into the Full Power of Fraud Features

Keeping pace with modern fraudsters will require the ability to create advanced attributes and features on the fly, without help from IT. DataVisor’s Features Platform empowers fraud and risk teams to leverage proven out-of-the box features or build powerful new ones with minimal effort and complexity, for immediate, reliable fraud prevention.

To learn more about DataVisor’s Feature Platform, request a demo to discuss your fraud prevention needs.