Disclosed are example embodiments of a methods and systems for analyzing and determining impact (or lack thereof) on any selected group or groups of employees of selected pay policies. An example includes a computer-implemented method for analyzing and determining impact on selected group of employees of selected pay policies. The method including receiving a first pay data for the selected group of employees. The method also including determining one or more controls for the selected group of employees. Additionally, the method including calculating an equitable pay range for the selected group of employees based on the one or more controls. The method also including receiving a user input, wherein the user input requests a second pay data relates to an employee, and is based on a selected control. The method also including calculating the second pay data; and sending the second pay data for display.

Provided may be a computer system for payroll management wherein the stored program instructions cause the processor to correlate each of a plurality of employees to one of a plurality of scheduled positions and a payroll type; determine a scheduled start time, a scheduled end time, a scheduled location-in location, and a scheduled location-out location based on a work schedule for each of the employees; receive, via an employee device, a first scan time and a clock-in time; determine an actual clock-in location based on a clock-in geolocation generated by the employee device upon the generation of the clock-in input; receive, via the employee device, a last scan time and a clock-out time; determine an actual clock-out location based on a clock-out geolocation generated by the employee device upon the generation of the clock-out input; and determine a location-in distance and determine a location-out distance.

A method for detecting anomalous data is provided. The method comprises collecting a training dataset comprising a number of transactional time series, wherein the time series comprise non-anomalous data entries for a specified transaction type. The training dataset is fed into a gated recurrent unit (GRU) network, which learns the data distribution for the transactional time series. The GRU predicts expected future values of the specified transaction type according to the learned data distribution. An upper bound and a lower bound for future values are calculated based a standard deviation of the predicted values. When new data entries of the specified transaction type are received their values are compared to the upper bound and the lower bound, and an error notification is provided if the values of the new data entries fall outside the upper bound or lower bound.

Systems and methods for time entry, management and billing are disclosed herein. In an embodiment, a method of collaborative time entry includes generating a first graphical user interface on a first user terminal that allows a first user to link a first time entry to a second user, storing in a memory at least a portion of time entry data from the first time entry, creating a second time entry on a second graphical user interface on a second user terminal, the second time entry including at least the portion of the time entry data from the first time entry, and generating a report including the first time entry and the second time entry.

A computerized payroll system is provided that includes a server having a processor configured to receive from an employee device or from a manager device, a record indicating a total number of hours worked by an employee during a pay period on shifts at different pay rates. The processor is further configured to determine an average pay rate for the total number of hours and any applicable overtime pay rate. The processor is further configured to calculate a benefit amount for the employee. The processor is further configured to display the calculated benefit, display a selector to redeem the calculated benefit, and receive user input via the selector of a request to redeem a requested portion of the calculated benefit, and responsive to receiving the request, initiate a redemption transaction to transfer the requested portion of the calculated benefit to an account identified by the user.

There are provided systems and methods for reducing account churn rate through intelligent collaborative filtering. A user may utilize an online account with the service provider to perform various actions and generate account usage data, such as usage of various service subcategories of the service provider or different subcategorizations for the user. Based on the current user's usage of services and other subcategorizations of the user, the user may be compared to other past users that have left the service provider utilizing collaborative filtering for matching users and user vectorization. For example, a matrix of the past users may be made based on the number of matching user that fall into the same categorizations. Each row of the matrix may be vectorized and compared to current user. If this comparison indicates potential attrition of the account, a remediation action may be taken.

A method for generating financial propensity outcomes using a machine learning model. The method may include receiving internal data on a client, the internal data comprises transactional data, behavioral data, demographic data, credit data, and communication data; receiving external data, the external data comprises publicly available data; training the machine learning model using historical financial data to model forecasts and predictive analytics; deploying the trained machine learning model and providing the internal data and the external data as data input to the trained machine learning model; and generating the financial propensity outcomes associated with the client from the trained machine learning model through forecasts and predictive analytics.

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.

A disbursement authorization data object processing system receives disbursement authorization data objects from a plurality of client devices. A real-time status reporting service is invoked to determine real-time status report data associated with the disbursement authorization data object. Using the real-time status report data, the disbursement authorization data object processing system invokes a disbursement authorization service to determine recipient instruction data which may include a recipient instructions status code. An disbursement instruction data object may then be transmitted to the client device that transmitted the disbursement authorization data object. The client device may facilitate the submission of additional documentation, selection of certain recipient instruction data, and/or the like to transmit to the disbursement authorization data object processing system in associated with a disbursement authorization data object.

The present disclosure relates to linking processing codes between platforms, and more particularly, to automatically record linking processing codes between platforms and methods of use. The method includes: obtaining a legacy processing code from a legacy system; obtaining a virtual code from a virtual entry table (VET) which corresponds with the legacy processing code; and mapping the legacy processing code to a target processing code using the virtual code from the VET.