Measuring Risk with Graph Intelligence
January 30, 2020
Machine learning and Regtech are popular topics these days. And, both play a big role in the IdentityMind platform.
Large amounts of data can be valuable, and simultaneously daunting. A key promise of Regtech is to empower you to use large amounts of data to drive decisions, reduce risk and prepare compliance responses. To fulfill this promise we developed Graph Intelligence.
Graph intelligence makes it possible to accurately perform risk assessment on digital identities. The eDNA engine within the IdentityMind platform takes attributes that belong to an individual and creates a graph representation of their digital identity. The platform then runs each identity through a series of steps for validation and analysis. The information is, in its graph identity form, more manageable and more easily analyzed by the IdentityMind platform, than when in its raw data form.
The platform provides a wide variety of analysis capabilities including a visual representation of the graph that can reveal patterns that show, for instance, fraud rings. However, it is the graph score that takes graph intelligence from great idea to a truly actionable tool for real time decisioning that is a valuable part of any fraud analyst’s tool kit.
The graph score is the result of applying supervised machine learning to the identity graphs. The models are informed in real-time and periodically trained to maintain the desired level of accuracy across a wide variety of scenarios.
Of course, there’s a lot more to say on how it works and on results…
For the full scoop, read our new guide: “Graph Intelligence: A Machine Learning Approach to Measure Risk on Digital Identities”.
