In some embodiments of the present disclosure, techniques are utilized that allow answers to be provided to end users such as health care consumers, based on benefit book documents. The benefit book documents, which do not initially contain machine-readable structural or semantic information, are processed in order to detect structure and create semantic content based on the structure. This semantic content may then be added to a graph that represents the information contained in the benefit book document. A computing device may then use the nodes of this graph to answer questions received from consumers, where templates that provide answers to the questions reference the nodes of the graph.

A data analytics system may include a first risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider. Similarly, a second risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider. A back-end application computer server may include a data mining engine that analyzes a set of electronic records in the first and second risk relationship data stores to identify flags corresponding to risk drivers. A predictive analytics engine may then calculate a risk score associated with the set of electronic records based on the associated entity attribute values and the identified flags corresponding to risk drivers. An insight platform may automatically generate a recommended action for the enterprise to lower the calculated risk score.

A data analytics system may include a first risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider. Similarly, a second risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider. A back-end application computer server may include a data mining engine that analyzes a set of electronic records in the first and second risk relationship data stores to identify flags corresponding to risk drivers. A predictive analytics engine may then calculate a risk score associated with the set of electronic records based on the associated entity attribute values and the identified flags corresponding to risk drivers. An insight platform may automatically generate a recommended action for the enterprise to lower the calculated risk score.

A multi-vendor risk relationship data store may contain electronic records representing a plurality of risk relationships between multiple vendors and an enterprise and, for each risk relationship, an electronic record identifier and a set of attribute values. An information sharing portal computer server may exchange an information request with a remote vendor platform. The information request may be, for example, associated with an employee of a sponsor associated with the vendor platform. The server may process the information request via a customized vendor-specific information sharing portal interface display and execute an algorithm to adjust, based on at least one attribute value, interactions with the vendor platform via the portal interface display. The server may also aggregate information associated with a plurality of vendor platforms and transmit the aggregated information to a remote administrator device associated with the enterprise.

A multi-vendor risk relationship data store may contain electronic records representing a plurality of risk relationships between multiple vendors and an enterprise and, for each risk relationship, an electronic record identifier and a set of attribute values. An information sharing portal computer server may exchange an information request with a remote vendor platform. The information request may be, for example, associated with an employee of a sponsor associated with the vendor platform. The server may process the information request via a customized vendor-specific information sharing portal interface display and execute an algorithm to adjust, based on at least one attribute value, interactions with the vendor platform via the portal interface display. The server may also aggregate information associated with a plurality of vendor platforms and transmit the aggregated information to a remote administrator device associated with the enterprise.

The present disclosure relates generally to utilizing paid time off. In one example, the systems and methods described herein may provide an infrastructure to repurpose paid time off into other uses, such as cash, travel, bill payments, and the like.

The present disclosure relates generally to utilizing paid time off. In one example, the systems and methods described herein may provide an infrastructure to repurpose paid time off into other uses, such as cash, travel, bill payments, and the like.

A system, method, and computer program product for setting an employee contribution to a retirement plan are disclosed. The method includes: assigning, by a computer system, each employee in a plurality of employees to one of a plurality of clusters by processing socio-economic information for the plurality of employees using machine learning to generate a machine learning model; determining, by the computer system, a benchmark for each cluster in the plurality of clusters from a characteristic of contributions to a retirement plan of the employees assigned to the cluster; identifying, by the computer system, a selected cluster in the plurality of clusters for a selected employee using the machine learning model; and controlling displaying, by a graphical user interface, the benchmark for the selected cluster to the selected employee.

A system, method, and computer program product for setting an employee contribution to a retirement plan are disclosed. The method includes: assigning, by a computer system, each employee in a plurality of employees to one of a plurality of clusters by processing socio-economic information for the plurality of employees using machine learning to generate a machine learning model; determining, by the computer system, a benchmark for each cluster in the plurality of clusters from a characteristic of contributions to a retirement plan of the employees assigned to the cluster; identifying, by the computer system, a selected cluster in the plurality of clusters for a selected employee using the machine learning model; and controlling displaying, by a graphical user interface, the benchmark for the selected cluster to the selected employee.

A system for interfacing predetermined services to a user at a fixed location includes a processing platform running an operating system. The system further includes a data store for storing configuration information for enabling the operating system to interface with available physical system resources through the physical system resource interface associated therewith. A communication resource for interfacing with the operating system allows communication of the operating system with a central office for downloading configuration information to selectively enable ones of the available physical system resources to interface with the operating system through associated ones of the physical system resource interfaces in accordance with the configuration information and the predetermined service selected by a user. A plurality of configurations is stored in the data store, and each is associated with a predetermined service and one or more of the available physical system resources.