A device may receive historical risk data identifying historical risks associated with entities, and historical compliance data identifying historical compliance actions performed by the entities. The device may train a machine learning model with the historical risk data and the historical compliance data to generate a structured semantic model, and may receive entity risk data identifying new and existing risks associated with an entity. The device may receive entity compliance data identifying new and existing compliance actions performed by the entity, and may process the entity risk data and the entity compliance data, with the structured semantic model, to determine risk and compliance insights for the entity. The risk and compliance insights may include insights associated with a key performance indicator, a compliance issue, a regulatory issue, an operational risk, a compliance risk, or a qualification of controls. The device may perform actions based on the risk and compliance insights.

A device may receive first information related to a transaction. The device may identify a first device from which to receive an image of a receipt related to the transaction. The device may provide, to the first device, a notification to cause the first device to perform a set of actions including activating a camera associated with the first device to capture the image of the receipt, or providing, for display, an instruction related to capturing the image of the receipt associated with the transaction. The device may receive, from the first device, the image of the receipt. The device may process the image of the receipt to perform an analysis of the transaction. The device may perform an action related to the transaction based on a result of processing the image of the receipt.

A system includes a processor and a memory with instructions. The instructions include, in response to receiving a zoned graph request, determining a current breakeven point on a current date based on a strike price of an option. The instructions include estimating a future strike price of the option based on an expiration date of the option, determining a future breakeven point on the expiration date of the option based on the estimated future strike price, and determining a range as a current price corresponding to the current breakeven point to a future price corresponding to the future breakeven point. The instructions include, for each time between the current date and the expiration date, determining a middle breakeven point at the corresponding time based on the range and generating a zoned graph including the current breakeven point, the future breakeven point, and each middle breakeven point at the corresponding times.

A system for processing electronic information is disclosed. The system includes a bracelet including a chip, a scannable code on the outer surface of the bracelet, and a PIN number on the inner surface of the bracelet. The system also includes a backend system for processing activation of the bracelet, for processing payments by a user of the bracelet, processing payroll payments to the user, processing access entry to a venue, or processing access to a cloud server. An interface is provided between the bracelet and the backend system. The interface receives data stored in the chip, the scannable code, or the PIN number.

A system and method are provided for validating data. The method is executed by a device having a data interface coupled to a processor and includes obtaining a validation set comprising at least one validation case, each validation case comprising at least one test condition. The method also includes obtaining, via the data interface, at least one data set to be validated using the validation set. The method also includes applying the validation set to the at least one data set to validate the data in the data set by, for each record in the at least one data set, validating a value in the record according to the at least one test condition. The method also includes outputting a validation result for each record.

The methods described herein are configured to obtain a first record pattern associated with the unidentified entity and select a second record pattern associated with an entity identifier of a known entity. Based on the first record pattern matching the second record pattern, the entity identifier of the known entity is associated to the unidentified entity to indicate that the unidentified entity and the known entity are the same. Determining the entity identifier of the unidentified entity enables the linking of separate identifier systems of data structures to facilitate communication and/or interaction between the data structures.

A method, apparatus, system, and computer program product for creating a forecasting model for payroll records. Payroll records are received for a group of employers. The payroll records comprise granular data parameters about employees of the group of employers. A forecasting model is created the that aligns the payroll records to high-level employment data. Creating the forecasting model includes identifying predictor variables from the granular data parameters of the payroll records. Creating the forecasting model includes generating a set of basis functions from the predictor variables. Creating the forecasting model includes combining the set of basis functions to create the forecasting model.

A system (100) includes one or more processors (113), one or more memory devices (114) operable with the one or more processors, and a communication device (1105) in communication with at least one terminal device (102,103,104,105) having a user interface (112). The one or more processors causing display of a visual simulation (123) in the user interface of one or more loss graphical objects (501) interacting with one or both of an entity type graphical object (601) or one or more loss mitigator graphical objects (602,603,604) as a function of a confidence level defined by a loss probability and a loss magnitude.

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 computer-implemented method and system that operates on a server includes a time and expense management interface for obtaining crew specific information for crews maintaining a utility distribution system. The server is coupled to a wireless communication infrastructure for receiving crew specific information. A portable electronic device receives a selection of a crew with members. The portable electronic device receives the identifier of the at least one or more members of the crew. Once verified, the portable device receives time entries and expense entries, for the at least one or more members of the crew. Business rules are applied to the time entries and expense entries. Once the business rules are satisfied, the time entries and expense entries are stored on the portable electronic device. When the portable electronic device is in proximity to the wireless communication infrastructure, the crew specific information is transmitted to server.