Abstract: Disclosed embodiments may include systems and methods for entity resolution. The system may receive a first plurality of identifiers and a taxpayer identification number. The system may determine one or more profiles preliminarily associated with the entity the one or more profiles including a plurality of data entries. The system may convert the plurality of data entries from a non-standardized format to a standardized format. The system may compare the first plurality of identifiers and the taxpayer identification number to the plurality of data entries to determine an entity match. In some examples, the system can vectorize the first plurality of identifiers and the taxpayer identification number into a first vectorized dataset and the plurality of data entries into one or more second vectorized datasets. The system may compare the first vectorized dataset to the one or more second vectorized dataset to determine an entity match.

Abstract: Disclosed embodiments may include a system for generating aggregate records. The system may retrieve data corresponding to a first user. The system may receive, from the first user, first sharing preference(s) associated with a second user. The system may extract first data subset(s) from the data based on the first sharing preference(s). The system may generate block(s) of a blockchain ledger. The system may append the first data subset(s) to at least a first block of the block(s) of the blockchain ledger. The system may provide the second user with access to the first data subset(s) on the at least first block.

Abstract: Disclosed embodiments may include a method for generating a trip plan with trip recommendations. The method may include generating and transmitting a graphical user interface to a user device and receiving, from the user device, criteria, which is then used to retrieve candidate data. The criteria and candidate data may then be used to generate potential leads using a machine learning model. The method may include generating a graphical user interface to display the potential leads and receive timing and preference data from the user. The method may further include generating, using a machine learning model, a plan based on the potential leads and other data. The plan may be presented to a user via graphical user interface, which may be used to modify and update the plan. The plan may be stored and accessed by the user at a later time through a user device.