Abstract: Aspects of the invention include threshold-triggered authority delegation. In one aspect, ownership of an asset is fractionalized into a plurality of tokens. It is detected that an ownership interest in an asset satisfies an ownership threshold. An owner of the ownership interest is identified. The owner and an asset valuation are sent to a General Partner corresponding to the asset. The General Partner can approve or not approve the asset valuation. When the asset valuation is approved, the identified owner can recapitalize the asset (or perform other actions) including refractionalizing ownership into a plurality of new tokens.

Abstract: The present invention extends to methods, systems, and computer program products for deriving asset characteristics and executing transactions based on user feedback and market context. A first set of data and a second set of data associated with an asset is collected. It is identified that part of the first set of data and the second set of data is incomplete. A market context assumption corresponding to the asset is utilized to complete the incomplete data. An artificial intelligence module validates relevancy of one or more items in the first data set and second data set. Subsequent and in response to validating relevancy of the one or more items, an asset valuation corresponding to the asset is derived based on the one or more items.

Abstract: The present invention extends to methods, systems, and computer program products for data retrieval and validation for onboarding private credit assets and deriving private credit asset characteristics. A first set of data associated with a private credit asset is collected. Identifiers associated with the first set of data are created. A second set of data associated with the private credit asset is collected based on the identifiers. The first set of data set and the second set of data are compared based on the identifiers. The first set of data is validated based on the comparison. An artificial intelligence module is trained as part of a continuous training cycle in view of validation findings. Concurrently with artificial intelligence module training, one or more characteristics of the private credit asset are derived from the first set of data.

Abstract: The present invention extends to methods, systems, and computer program products for data retrieval and validation for asset onboarding and deriving asset characteristics. An artificial intelligence (AI) framework streamlines the review and extraction of items (e.g., fields) from, for example, legal and financial documents, across industries. AI agents can operate both ahead or and/or in parallel with human teams, leveraging prior validated extractions to reinforce expected fields and also discover new fields used for asset characteristic derivation. Use of AI agents reduces upfront (human) subject matter expertise, accelerates document onboarding, and facilitates scalable, continuous schema evolution concurrently with deriving asset characteristics.

Abstract: In certain aspects, a computer-implemented method includes monitoring data entries written on a database. The computer-implemented method includes recording at least part of the data entries written on the database to a blockchain.

Abstract: A system and method for completing a sale of digital shares representing an interest in an asset in an exchange, wherein the exchange comprising a transfer agent, includes registering, by the transfer agent, a user on an exchange; presenting to the user, by the transfer agent an interface allowing the user to select an interest in an asset to sell; communicating, by the transfer agent, with a broker dealer to transmit a sell contract to the user; pairing, by the transfer agent, a buyer with the user to sell the interest in the asset to the buyer; and executing, by the transfer agent, a sale of digital shares representing the interest in the asset.

Abstract: The present invention extends to methods, systems, and computer program products for data retrieval and validation for asset onboarding and deriving asset characteristics. A first set of data associated with an asset is collected. Identifiers associated with the first set of data are created. A second set of data associated with the asset is collected based on the identifiers. The first set of data set and the second set of data are compared based on the identifiers. The first set of data is validated based on the comparison. An artificial intelligence module is trained as part of a continuous training cycle in view of validation findings. Concurrently with artificial intelligence module training, one or more characteristics of the asset are derived from the first set of data.

Abstract: The present invention extends to transaction handling in view of settlement factor compliance. Security action triggers subject pre-settlement transactions to one or more supplemental security actions prior to settlement approval. When it is determined that an identified factor impacts transaction settlement, a flag is set. The flag denotes that pre-settlement transactions associated with specified transaction data are subject to the one or more supplemental security actions prior to settlement. A transaction is reviewed, including interrupting the flow of the transaction and subjecting the transaction to the one or more supplemental security actions. The transaction is allowed to resume and progress towards settlement or is killed based on outcomes of the one or more supplemental security actions.

Abstract: The present invention extends to methods, systems, and computer program products for data retrieval and validation for onboarding private credit assets and deriving private credit asset characteristics. A first set of data associated with a private credit asset is collected. Identifiers associated with the first set of data are created. A second set of data associated with the private credit asset is collected based on the identifiers. The first set of data set and the second set of data are compared based on the identifiers. The first set of data is validated based on the comparison. An artificial intelligence module is trained as part of a continuous training cycle in view of validation findings. Concurrently with artificial intelligence module training, one or more characteristics of the private credit asset are derived from the first set of data.

Abstract: The present invention extends to methods, systems, and computer program products for data retrieval and validation for onboarding private credit assets and deriving private credit asset characteristics. A first set of data associated with a private credit asset is collected. Identifiers associated with the first set of data are created. A second set of data associated with the private credit asset is collected based on the identifiers. The first set of data set and the second set of data are compared based on the identifiers. The first set of data is validated based on the comparison. An artificial intelligence module is trained as part of a continuous training cycle in view of validation findings. Concurrently with artificial intelligence module training, one or more characteristics of the private credit asset are derived from the first set of data.

Abstract: The present invention extends to transaction handling in view of settlement factor compliance. Security action triggers subject pre-settlement transactions to one or more supplemental security actions prior to settlement approval. When it is determined that an identified factor impacts transaction settlement, a flag is set. The flag denotes that pre-settlement transactions associated with specified transaction data are subject to the one or more supplemental security actions prior to settlement. A transaction is reviewed, including interrupting the flow of the transaction and subjecting the transaction to the one or more supplemental security actions. The transaction is allowed to resume and progress towards settlement or is killed based on outcomes of the one or more supplemental security actions.

Abstract: A system and method for completing a sale of digital shares representing an interest in an asset in an exchange, wherein the exchange comprising a transfer agent, includes registering, by the transfer agent, a user on an exchange; presenting to the user, by the transfer agent an interface allowing the user to select an interest in an asset to sell; communicating, by the transfer agent, with a broker dealer to transmit a sell contract to the user; pairing, by the transfer agent, a buyer with the user to sell the interest in the asset to the buyer; and executing, by the transfer agent, a sale of digital shares representing the interest in the asset.

Abstract: A system and method for completing a sale of digital shares representing an interest in an asset in an exchange, wherein the exchange comprising a transfer agent, includes registering, by the transfer agent, a user on an exchange; presenting to the user, by the transfer agent an interface allowing the user to select an interest in an asset to sell; communicating, by the transfer agent, with a broker dealer to transmit a sell contract to the user; pairing, by the transfer agent, a buyer with the user to sell the interest in the asset to the buyer; and executing, by the transfer agent, a sale of digital shares representing the interest in the asset.

Abstract: The present invention extends to methods, systems, and computer program products for data retrieval and validation for asset onboarding and deriving asset characteristics. A first set of data associated with an asset is collected. Identifiers associated with the first set of data are created. A second set of data associated with the asset is collected based on the identifiers. The first set of data set and the second set of data are compared based on the identifiers. The first set of data is validated based on the comparison. An artificial intelligence module is trained as part of a continuous training cycle in view of validation findings. Concurrently with artificial intelligence module training, one or more characteristics of the asset are derived from the first set of data using one or more AI agents.

Abstract: In certain aspects, a computer-implemented method includes monitoring data entries written on a database. The computer-implemented method includes recording at least part of the data entries written on the database to a blockchain.

Abstract: The present invention extends to methods, systems, and computer program products for data retrieval and validation for onboarding private credit assets and deriving private credit asset characteristics. A first set of data associated with a private credit asset is collected. Identifiers associated with the first set of data are created. A second set of data associated with the private credit asset is collected based on the identifiers. The first set of data set and the second set of data are compared based on the identifiers. The first set of data is validated based on the comparison. An artificial intelligence module is trained as part of a continuous training cycle in view of validation findings. Concurrently with artificial intelligence module training, one or more characteristics of the private credit asset are derived from the first set of data.

Abstract: The present invention extends to methods, systems, and computer program products for data retrieval and validation for onboarding private credit assets and deriving private credit asset characteristics. A first set of data associated with a private credit asset is collected. Identifiers associated with the first set of data are created. A second set of data associated with the private credit asset is collected based on the identifiers. The first set of data set and the second set of data are compared based on the identifiers. The first set of data is validated based on the comparison. An artificial intelligence module is trained as part of a continuous training cycle in view of validation findings. Concurrently with artificial intelligence module training, one or more characteristics of the private credit asset are derived from the first set of data.

Abstract: The present invention extends to methods, systems, and computer program products for data retrieval and validation for asset onboarding and deriving asset characteristics. A first set of data associated with an asset is collected. Identifiers associated with the first set of data are created. A second set of data associated with the asset is collected based on the identifiers. The first set of data set and the second set of data are compared based on the identifiers. The first set of data is validated based on the comparison. An artificial intelligence module is trained as part of a continuous training cycle in view of validation findings. Concurrently with artificial intelligence module training, one or more characteristics of the asset are derived from the first set of data.

Abstract: The present invention extends to transaction handling in view of settlement factor compliance. Security action triggers subject pre-settlement transactions to one or more supplemental security actions prior to settlement approval. When it is determined that an identified factor impacts transaction settlement, a flag is set. The flag denotes that pre-settlement transactions associated with specified transaction data are subject to the one or more supplemental security actions prior to settlement. A transaction is reviewed, including interrupting the flow of the transaction and subjecting the transaction to the one or more supplemental security actions. The transaction is allowed to resume and progress towards settlement or is killed based on outcomes of the one or more supplemental security actions.

Abstract: In certain aspects, a computer-implemented method includes monitoring data entries written on a database. The computer-implemented method includes recording at least part of the data entries written on the database to a blockchain.