Abstract: Systems and methods for efficient association of related entities. The method may comprise accessing a database of records, using a processor, to identify a set of unoptimized entities represented by one or more nodes in a graph model, a connection between a first node and a second node in the one or more nodes representing an association between a first entity represented by the first node and a second entity represented by the second node; determining the first entity is unoptimized; and determining a set of related entities for the unoptimized first entity in the graph model, the graph model having at least one common entity with a corresponding label model.

Abstract: Systems and methods for efficient association of related entities. The method may comprise accessing a database of records, using a processor, to identify a set of unoptimized entities represented by one or more nodes in a graph model, a connection between a first node and a second node in the one or more nodes representing an association between a first entity represented by the first node and a second entity represented by the second node; determining the first entity is unoptimized; and determining a set of related entities for the unoptimized first entity in the graph model, the graph model having at least one common entity with a corresponding label model.

Abstract: A computer-implemented fraud detection method and system for periodically identifying network associations in a consumer population at a national credit reporting agency and computing associated network level variables related to credit use and potential first party fraud for the consumer population. In response to receiving a request for a target account from among the consumer population the computer-implemented system retrieves credit report for the target account and computes tradeline or account level variables related to credit use and potential fraudulent behavior. A fraud score is calculated based on a combined evaluation of the network level variables and the tradeline or account level variables.

Abstract: In an aspect, an entity group including associations of entities grouped according to a measure of similarity can be received. The entities can include units of data extracted from a set of documents. A vector can be assembled. Assembly of the vector can include evaluation of a predefined entity analytic using the received entity group. The vector can be provided to a second analytic. Related apparatus, systems, techniques, and articles are also described.

Abstract: Records are obtained from sources and typed datasets are assembled based on the obtained records. Relationships are identified among the typed datasets and at least one network comprising nodes and edges are assembled based on the identified relationships. A network analyzer assembles a vector and the vector is passed to analytics. The analytics then generate an output that is provided to a user or other device in the form of data.

Abstract: Records are obtained from sources and typed datasets are assembled based on the obtained records. Relationships are identified among the typed datasets and at least one network comprising nodes and edges are assembled based on the identified relationships. A network analyzer assembles a vector and the vector is passed to analytics. The analytics then generate an output that is provided to a user or other device in the form of data.