Abstract: Provided are systems and methods for recommending and prioritizing jobs to pursue, as well as skills, credentials, education, and the like to obtain, targeted to both individuals and consultants, coaches, etc. who may help those individuals. The prioritizing and the recommending may be performed based on a skills graph that includes nodes representing entities, and edges annotated with shared skills information between the entities. Thus, the skills graph can be used to find entities that are similar to each other in terms of skills.

Abstract: Provided are systems and methods for building a skills certificate. In some embodiments, the host platform may be a web server, a cloud platform, a database, or the like, which hosts an application such as a mobile application, a progressive web application, or the like.

Abstract: Provided are systems and methods for recommending job opportunities via a machine learning engine which is coupled to an enrichment pipeline. The enrichment pipeline can add skills information and other beneficial data to enrich a job profile of a user and use the enriched record to predict an optimal job opportunity or set of opportunities. In one example, the method includes receiving a description of employment data, identifying a unique identifier of a job profile based on the description of the employment data, querying a database with the unique identifier to retrieve a list of skills from the database and that are mapped to the unique identifier, transforming the list of skills from into a skills vector, determining one or more optimal job opportunities via execution of a ML model on the skills vector, and outputting information about the optimal job opportunities via a user interface.

Abstract: Provided are systems and methods for verifying an identity of a user based on a data mesh created from various sources of truth. In one example, a method may include establishing, via a host platform, a first and a authenticated communication channel between a host server of a user account and a host server of a second user account, retrieving, via the first and second authenticated communication channels, PII of the user from the first and second user accounts and combining the PII into a meshed data set, determining a difference between the PII within the meshed data set, and verify an identity of the user based on the determined difference between the PII within the meshed data set and transmitting the verification to a computer system.

Abstract: Provided are systems and methods for generating propensity-driven employment-based search results. In one example, a method may include retrieving consumption data of a user associated with the user device and geographic location data of at least one of the user and the user device, the consumption data comprising behavior of the user captured over time, predicting one or more value-generating opportunities for the user from among a pool of value-generating opportunities via execution of a predictive model, where the predictive model determines a propensity of the user to respond to the value-generating opportunities based on the behavioral attributes included in the retrieved consumption data of the user and the geographical location data, and transmitting the one or more predicted value-generating opportunities to the user device via a content channel between the server and the user device.

Abstract: Provided are systems and methods for reconciling transactions from different financial accounts via machine learning. In one example, a method may include storing a data file comprising a plurality of electronic transactions and a plurality of transaction records corresponding to the plurality of electronic transactions in memory, inputting the data file into a first machine learning model and identifying respective transaction attributes of the plurality of transaction records based on the input data file, determining, via execution of a second machine learning model, that a first transaction and a second transaction in the data file correspond to a common transaction based on attributes of the first and second transactions which are identified from respective transactions records of the first and second transactions, and modifying the data file to indicate that the first and second transactions correspond to the common transaction and storing the modified data file in the memory.

Abstract: Provided are systems and methods which can use machine learning to draw additional inferences about transaction records from transaction strings. The inferred data can be used to build a classification model configured to map transaction string to predefined categories. In one example, a method may include receiving a file comprising transaction strings corresponding to a plurality of transaction records, executing the machine learning model on the transaction strings to identify a plurality of categories associated with the transaction strings, generating a classifier model that comprises patterns of keywords from the transactions strings mapped to the plurality of identified categories, respectively, and storing the classifier model in the data store.

Abstract: Provided are systems and methods that rely on machine learning to recommend employment opportunities. In one example, a method may include identifying, via execution of a first machine learning model, income data and spending data of a user, identifying, via execution of a second machine learning model, skill attributes of the user, determining, via execution of a third machine learning model, a recommended job for the user, where the determining comprises inputting the outputs from the first and second machine learning models into the third machine learning model, and displaying, via a user interface, a description of the recommended job.

Abstract: The example embodiments are directed to a method and system which can identify shifts within employment data and use the identified shifts to organize data and execute additional analytics on the data. In one example, the method may include receiving geopositional data and timing data from a computing device associated with a user, identifying a set of tasks that were performed based on changes in one or more of the geopositional data and the timing data, determining that a first subset of tasks from the set of tasks are included in a first shift and a second subset of tasks from the set of tasks are included in a second shift based on a gap in time between a last task of the first subset and a first task of the second subset, and storing timing values of the determined first and second shifts in a data store.

Abstract: Provided are systems and methods that rely on machine learning to recommend employment opportunities. In one example, a method may include identifying, via execution of a first machine learning model, income data and spending data of a user, identifying, via execution of a second machine learning model, skill attributes of the user, determining, via execution of a third machine learning model, a recommended job for the user, where the determining comprises inputting the outputs from the first and second machine learning models into the third machine learning model, and displaying, via a user interface, a description of the recommended job.

Abstract: Provided are systems and methods for determining behaviors of a user that are associated with earning secondary income. In one example, a method may include receiving a request from a user device, identifying productivity attributes and consumption attributes of a user associated with the user device based on behavior of the user captured over time, determining a recommended value-generating behavior for the user via execution of a collaborative filtering model, where the collaborative filtering model receives the productivity and consumption attributes of the user and determines the recommended value-generating behavior based on historical value-generating behavior of other users having similar productivity attributes and consumption attributes, and transmitting the recommended value-generating behavior to the user device via a content channel between the server and the user device.

Abstract: Provided are systems and methods for generating propensity-driven employment-based search results. In one example, a method may include retrieving consumption data of a user associated with the user device and geographic location data of at least one of the user and the user device, the consumption data comprising behavior of the user captured over time, predicting one or more value-generating opportunities for the user from among a pool of value-generating opportunities via execution of a predictive model, where the predictive model determines a propensity of the user to respond to the value-generating opportunities based on the behavioral attributes included in the retrieved consumption data of the user and the geographical location data, and transmitting the one or more predicted value-generating opportunities to the user device via a content channel between the server and the user device.