A method for generating a communicable disease policy plan by using machine learning. The process identifies a number of workplace policies for a number of business entities. The workplace policies comprise a number of dimensions of data collected from a number of sources. The process collects employment data for each of the business entities. The employment data includes sick leave data about employees of the plurality of business entities. The process determines metrics for the sick leave data during a given time interval; simultaneously models the workplace policies and the metrics for the sick leave data to identify correlations among the number of dimensions of data and generalize rules for predicting effective workplace policies; predicts a number of effective workplace policies for a particular business entity; and generates a communicable disease policy plan for the particular business entity based on the number of effective workplace policies.

This invention relates to systems and methods for imputing missing data in a dataset, for determining a health condition of a person, and for calculating an insurance premium. In particular, the method described herein employs a trained autoencoder system which is configured to receive an input dataset comprising input data which has data missing therefrom. In a preferred example embodiment, the input data contains data associated with a person and the missing data is an HIV and/or Syphilis status of the person. The trained autoencoder system is configured to impute the missing data from the input dataset, which in the case of the preferred example embodiment is to impute or predict the HIV and/or Syphilis status of the person.

Methods and systems may be associated with an enhanced Health Saving Account (“HSA”) offered by an employer to a plurality of employees. An advance limit calculation platform may access information in a member data store that contains information associated with the plurality of employees. The member data store may include, for each employee, an employee identifier, a communication address, and employment characteristics that were received from an employer human resources system. The advance limit calculation platform may then calculate, for each employee, an advance limit based on the employment characteristics (e.g., salary information, a date of hire, payroll deduction availability, a number of dependents, etc.). The system may then arrange to transmit, to each employee via the communication address and a distributed communication network, enhanced HSA information including the calculated advance limit. Moreover, embodiments may provide a scoring model and/or purse amount for the enhanced HSA.

An investment learning system is provided and includes a personal computer device having a single general user interface and a central processing unit and a performance management server connected to the personal computer device and having a non-transitory computer readable storage device having a database module for calculating and displaying simulated investment snapshot for a custom investment period utilizing historical data and user controlled inputs, and a central processing unit connected to the personal computer device and the computer readable storage device, and running a plurality of core modules to map and link individual action items to calculate and generate integrated financial and managerial summaries. The plurality of core modules include a first input module to select a time period and a result module to generate simulated financial results based on historical events during that selected time period.

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.

Disclosed herein are system, method, and computer program product embodiments for configuring a multi-purse (MP) table for representing a benefit plan including multiple accounts and respective eligible services. An embodiment operates by storing MP table configurations within a management database. Upon receiving a plan offering for an employee, an MP table selection component of the management system may select an MP table configuration from the stored MP table configurations. The MP table selection component may configure an MP table using the selected MP table configuration by enabling access indicators of respective account types of the MP table that satisfy at least one of the received plurality of accounts in the plan offering. Upon configuration of the MP table, a distribution component of the management system may send the MP configuration to a card vendor configured to process claims received at the card vendor.

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.

Increased regulation reflects individuals' growing concerns about sharing their health data. But, if left unaddressed, these regulations act as a barrier to healthcare researchers and providers accessing the real-world health data they need for AI-powered health advances. Accordingly, the inventors have established a decentralized ledger based self-sovereign data management platform for individuals. Via the platform an individual is provided a personalized health wallet to supporting self-sovereign data management giving them control and custody of their own health data. The platform provides for data credentialing to ensure quality and support of verifiable claims, e.g., about an individuals' consent to share and/or a third party research project having ethics approval, zero-knowledge proofs to protect individual privacy and promote secure data sharing, and support for privacy-preserving audit, accountability and compliance relating individuals' consent and data sharing.