Machine learning-based graph analytics for user evaluation

Abstract

Aspects of the present disclosure relate to machine learning-based graph analytics for account evaluation. In examples, information associated with a user is stored in a graph datastore, which may include one or more account nodes and associated transaction nodes. Nodes within the graph datastore may be associated using edges that include identification information for an associated user. Accordingly, it may be possible to identify a subpart of the graph associated with a user that includes associated user identifiers and historical activity, which may be processed to generate a feature vector. The feature vector may be processed using a machine learning model to generate a set of reputation metrics for the user. The resulting set of reputation metrics may thus be used to determine whether to permit access to a resource or service by the user, or whether the user is permitted to create a new account, among other examples.

Dependencies are inferred from the claim text.