Abstract: A leasing AI platform receives a message pertaining to leasing a real estate unit, generates a token matrix representing at least a portion of the message, and provide the token matrix as an input to a trained machine learning model. An output of the trained machine learning model comprises an indication of a first category associated with the at least the portion of the message. The leasing AI platform identifies one or more actions associated with the first category, the actions pertaining to leasing the real estate unit, and automatically executes the one or more actions without human involvement in response to receiving the message.

Abstract: A method comprising creating and storing, in computer memory, a financial graph having nodes and edges, wherein the nodes include first nodes representing assets and second nodes representing any one or more of accounts in which one or more of the assets are held, individuals who own one or more of the assets, or legal entities who own one or more of the assets; obtaining, from an asset custodian data source, asset transaction data associated with one or more of the assets represented by the first nodes of the financial graph; transforming the asset transaction data into one or more stored transaction objects, wherein each transaction object is associated with a plurality of transaction effect objects; wherein each of the plurality of transaction effect objects is associated with a particular edge in the financial graph and represents one of a credit to an account type associated with the particular edge and a debit from an account type associated with the particular edge.

Abstract: A leasing AI platform receives a message pertaining to leasing a real estate unit, generates a token matrix representing at least a portion of the message, and provide the token matrix as an input to a trained machine learning model. An output of the trained machine learning model comprises an indication of a first category associated with the at least the portion of the message. The leasing AI platform identifies one or more actions associated with the first category, the actions pertaining to leasing the real estate unit, and automatically executes the one or more actions without human involvement in response to receiving the message.

Abstract: A leasing AI platform receives a message pertaining to leasing a real estate unit, generates a token matrix representing at least a portion of the message, and provide the token matrix as an input to a trained machine learning model. An output of the trained machine learning model comprises an indication of a first category associated with the at least the portion of the message. The leasing AI platform identifies one or more actions associated with the first category, the actions pertaining to leasing the real estate unit, and automatically executes the one or more actions without human involvement in response to receiving the message.

Abstract: A method comprising creating and storing, in computer memory, a financial graph having nodes and edges, wherein the nodes include first nodes representing assets and second nodes representing any one or more of accounts in which one or more of the assets are held, individuals who own one or more of the assets, or legal entities who own one or more of the assets; obtaining, from an asset custodian data source, asset transaction data associated with one or more of the assets represented by the first nodes of the financial graph; transforming the asset transaction data into one or more stored transaction objects, wherein each transaction object is associated with a plurality of transaction effect objects; wherein each of the plurality of transaction effect objects is associated with a particular edge in the financial graph and represents one of a credit to an account type associated with the particular edge and a debit from an account type associated with the particular edge.

Abstract: A method comprising creating and storing, in computer memory, a financial graph having nodes and edges, wherein the nodes include first nodes representing assets and second nodes representing any one or more of accounts in which one or more of the assets are held, individuals who own one or more of the assets, or legal entities who own one or more of the assets; obtaining, from an asset custodian data source, asset transaction data associated with one or more of the assets represented by the first nodes of the financial graph; transforming the asset transaction data into one or more stored transaction objects, wherein each transaction object is associated with a plurality of transaction effect objects; wherein each of the plurality of transaction effect objects is associated with a particular edge in the financial graph and represents one of a credit to an account type associated with the particular edge and a debit from an account type associated with the particular edge.

Abstract: A leasing AI platform receives a message pertaining to leasing a real estate unit, generates a token matrix representing at least a portion of the message, and provide the token matrix as an input to a trained machine learning model. An output of the trained machine learning model comprises an indication of a first category associated with the at least the portion of the message. The leasing AI platform identifies one or more actions associated with the first category, the actions pertaining to leasing the real estate unit, and automatically executes the one or more actions without human involvement in response to receiving the message.

Abstract: A method comprising creating and storing, in computer memory, a financial graph having nodes and edges, wherein the nodes include first nodes representing assets and second nodes representing any one or more of accounts in which one or more of the assets are held, individuals who own one or more of the assets, or legal entities who own one or more of the assets; obtaining, from an asset custodian data source, asset transaction data associated with one or more of the assets represented by the first nodes of the financial graph; transforming the asset transaction data into one or more stored transaction objects, wherein each transaction object is associated with a plurality of transaction effect objects; wherein each of the plurality of transaction effect objects is associated with a particular edge in the financial graph and represents one of a credit to an account type associated with the particular edge and a debit from an account type associated with the particular edge.

Abstract: A method comprising creating and storing, in computer memory, a financial graph having nodes and edges, wherein the nodes include first nodes representing assets and second nodes representing any one or more of accounts in which one or more of the assets are held, individuals who own one or more of the assets, or legal entities who own one or more of the assets; obtaining, from an asset custodian data source, asset transaction data associated with one or more of the assets represented by the first nodes of the financial graph; transforming the asset transaction data into one or more stored transaction objects, wherein each transaction object is associated with a plurality of transaction effect objects; wherein each of the plurality of transaction effect objects is associated with a particular edge in the financial graph and represents one of a credit to an account type associated with the particular edge and a debit from an account type associated with the particular edge.

Abstract: A method comprising creating and storing, in computer memory, a financial graph having nodes and edges, wherein the nodes include first nodes representing assets and second nodes representing any one or more of accounts in which one or more of the assets are held, individuals who own one or more of the assets, or legal entities who own one or more of the assets; obtaining, from an asset custodian data source, asset transaction data associated with one or more of the assets represented by the first nodes of the financial graph; transforming the asset transaction data into one or more stored transaction objects, wherein each transaction object is associated with a plurality of transaction effect objects; wherein each of the plurality of transaction effect objects is associated with a particular edge in the financial graph and represents one of a credit to an account type associated with the particular edge and a debit from an account type associated with the particular edge.

Abstract: A method comprising creating and storing, in computer memory, a financial graph having nodes and edges, wherein the nodes include first nodes representing assets and second nodes representing any one or more of accounts in which one or more of the assets are held, individuals who own one or more of the assets, or legal entities who own one or more of the assets; obtaining, from an asset custodian data source, asset transaction data representing a plurality of financial transaction events; identifying a financial transaction type represented by one or more of the plurality of financial transaction events; generating, based on the identified financial transaction type, a plurality of transaction effect objects; wherein each of the plurality of transaction effect objects is associated with a particular edge in the financial graph and represents one of a credit to an account type associated with the particular edge and a debit from an account type associated with the particular edge.

Abstract: A method comprising creating and storing, in computer memory, a financial graph having nodes and edges, wherein the nodes include first nodes representing assets and second nodes representing any one or more of accounts in which one or more of the assets are held, individuals who own one or more of the assets, or legal entities who own one or more of the assets; obtaining, from an asset custodian data source, asset transaction data associated with one or more of the assets represented by the first nodes of the financial graph; transforming the asset transaction data into one or more stored transaction objects, wherein each transaction object is associated with a plurality of transaction effect objects; wherein each of the plurality of transaction effect objects is associated with a particular edge in the financial graph and represents one of a credit to an account type associated with the particular edge and a debit from an account type associated with the particular edge.

Abstract: A method comprising creating and storing, in computer memory, a financial graph having nodes and edges, wherein the nodes include first nodes representing assets and second nodes representing any one or more of accounts in which one or more of the assets are held, individuals who own one or more of the assets, or legal entities who own one or more of the assets; obtaining, from an asset custodian data source, asset transaction data representing a plurality of financial transaction events; identifying a financial transaction type represented by one or more of the plurality of financial transaction events; generating, based on the identified financial transaction type, a plurality of transaction effect objects; wherein each of the plurality of transaction effect objects is associated with a particular edge in the financial graph and represents one of a credit to an account type associated with the particular edge and a debit from an account type associated with the particular edge.