Abstract: A fraud detection system is disclosed that detects potential fraudulent behavior associated a user account by identifying attributes of the user account that share attributes with one or more known fraudulent user accounts. The set of shared attributes for a user account are identified by constructing a bipartite graph comprising a set of user account nodes and a set of attribute nodes associated with the set of user account nodes. A match score for the user account is computed based on the set of shared attributes. Actions to be taken for the user account are identified based on the match score. The actions may include tagging the user account as potentially fraudulent. The actions can be used by a user (e.g., an administrator) of an organization to more intelligently determine appropriate measures to be taken for the potentially fraudulent user account.

Abstract: The present disclosure describes techniques for, in addition to a “global model” that is trained using training data, training one or more “segmented” machine learning (ML) models using subsets or segments of the training data, wherein a segmented ML model is trained using a segment or subset of the training data that satisfies a particular condition. Both the trained global model, and where applicable, one or more trained segmented models are used to make predictions in a production environment.

Abstract: A fraud detection system is disclosed that detects potential fraudulent behavior associated a user account by identifying attributes of the user account that share attributes with one or more known fraudulent user accounts. The set of shared attributes for a user account are identified by constructing a bipartite graph comprising a set of user account nodes and a set of attribute nodes associated with the set of user account nodes. A match score for the user account is computed based on the set of shared attributes. Actions to be taken for the user account are identified based on the match score. The actions may include tagging the user account as potentially fraudulent. The actions can be used by a user (e.g., an administrator) of an organization to more intelligently determine appropriate measures to be taken for the potentially fraudulent user account.