Abstract: Provided are systems and methods for providing insights to users of a mobile application based on triggers that may be detected from within the data or externally. In one example, a method may include receiving, via a message transmitted to a uniform resource locator (URL) of a host platform, information about a user of a software application, reading a plurality of records associated with the user from the received information, the plurality of records comprising data and a plurality of timestamps, respectively, generating derived data based on the data included in the plurality of records, converting the plurality of timestamps into a single timing identifier that represents all of the plurality of timestamps, and transmitting, to a computing system, a notification comprising the derived data and the new timing identifier.

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 providing insights to users of a mobile application based on triggers that may be detected from within the data or externally. In one example, a method may include receiving, via a message transmitted to a uniform resource locator (URL) of a host platform, information about a user of a software application, reading a plurality of records associated with the user from the received information, the plurality of records comprising data and a plurality of timestamps, respectively, generating derived data based on the data included in the plurality of records, converting the plurality of timestamps into a single timing identifier that represents all of the plurality of timestamps, and transmitting, to a computing system, a notification comprising the derived data and the new timing identifier.

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 identifying a counterpart entity of a transaction from a transaction string. In one example, a method may include identifying a transaction string from an account of a target entity, the transaction string comprising a record of a payment that changed a balance of the account, executing a machine learning model based on the transaction string to determine a counterpart entity of the transaction with respect to the target entity, wherein, during execution, the machine learning model receives the transaction string as input and outputs an identifier of the counterpart entity, generating a data structure comprising a first field that stores the transaction string and a second field that stores an identifier of the counterpart entity, and storing the data structure within a file.

Abstract: Provided are systems and methods for identifying unclaimed sources of funds such as employers, gig opportunities, businesses, and the like. The process can be used as part of a larger process that may also include fraud checks, deduplication of data, verification of users, analytical insight, and the like. In one example, a method may include establishing a communication channel with a third-party data source via an application programming interface (API), ingesting data records of the user from the third-party data source via the established communication channel based on an account identifier, identifying an unclaimed source of income based on a data value stored within the ingested data records, and displaying an identifier of the unclaimed source of income and an input mechanism which is configured to confirm the identified unclaimed source of income.

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 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 for identifying a counterpart entity of a transaction from a transaction string. In one example, a method may include identifying a transaction string from an account of a target entity, the transaction string comprising a record of a payment that changed a balance of the account, executing a machine learning model based on the transaction string to determine a counterpart entity of the transaction with respect to the target entity, wherein, during execution, the machine learning model receives the transaction string as input and outputs an identifier of the counterpart entity, generating a data structure comprising a first field that stores the transaction string and a second field that stores an identifier of the counterpart entity, and storing the data structure within a file.

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 providing insights to users of a mobile application based on triggers that may be detected from within the data or externally. In one example, a method may include receiving, via a message transmitted to a uniform resource locator (URL) of a host platform, information about a user of a software application, reading a plurality of records associated with the user from the received information, the plurality of records comprising data and a plurality of timestamps, respectively, generating derived data based on the data included in the plurality of records, converting the plurality of timestamps into a single timing identifier that represents all of the plurality of timestamps, and transmitting, to a computing system, a notification comprising the derived data and the new timing identifier.