Abstract: In certain aspects, a computer-implemented method includes monitoring data entries written on a private Web2 ledger. The computer-implemented method includes recording the data entries written on the private Web2 ledger to an immutable public Web3 ledger.

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 automated computing resource allocation and comparison validating ownership and transaction completion. Transaction data for a transaction is accessed. Automatically and without user intervention, a transaction is validated in view of asset ownership conditions. Transaction validation can include comparing memory locations to validate compliance with asset ownership conditions. Upon validation, wallet and ledger resources can be automatically allocated. The transaction can be completed using the wallet and ledger resources, including electronically moving tokens between digital wallets, documenting transaction completion in a mutable ledger, and automatically partially syncing transaction documentation from the mutable ledger to an immutable ledger.

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 of the disclosure, a computer-implemented method includes associating a token with a token identification and associating the token identification with corresponding metadata of the token. The method includes updating and logging changes in the metadata as attributes evolve. The method includes updating token value based on the changes and publishing a real-time token value with selected information about the changes.

Abstract: In certain aspects of the disclosure, a computer-implemented method includes associating a token with a token identification and associating the token identification with corresponding metadata of the token. The method includes updating and logging changes in the metadata as attributes evolve. The method includes updating token value based on the changes and publishing a real-time token value with selected information about the changes.

Abstract: In certain aspects of the disclosure, a computer-implemented method includes associating a token with a token identification and associating the token identification with corresponding metadata of the token. The method includes updating and logging changes in the metadata as attributes evolve. The method includes updating token value based on the changes and publishing a real-time token value with selected information about the changes.

Abstract: In certain aspects of the disclosure, a computer-implemented method includes collecting, via an artificial intelligence module, a first set of data associated with an asset and creating identifiers associated with the first set of data. The method includes collecting a second set of data associated with the asset based on the identifiers and comparing the first set of data and the second set of data based on the identifiers. The method includes determining validation of the first set of data based on the comparison and generating a result of approval or rejection based on the comparison.

Abstract: In certain aspects of the disclosure, a computer-implemented method includes receiving an initial order based on a user input. The method includes mapping out correct steps from the initial order to settlement. The method includes validating instructions at each step. The method includes recording data pertaining to permit the settlement of the transaction.

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: In certain aspects of the disclosure, a computer-implemented method includes receiving information regarding characteristics of an asset. The method includes tokenizing at least one digital asset representing fractional shares in the asset. The method includes determining the initial share price. The method includes updating a capitalization table based on the number shares and share price. The method includes performing a transaction based on share price. Systems and machine-readable media are also provided.

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 of the disclosure, a computer-implemented method includes collecting, via an artificial intelligence module, a first set of data associated with a private credit asset and creating identifiers associated with the first set of data. The method includes collecting a second set of data associated with the private credit asset based on the identifiers and comparing the first set of data and the second set of data based on the identifiers. The method includes determining validation of the first set of data based on the comparison and generating a result of approval or rejection based on the comparison.

Abstract: In certain aspects of the disclosure, a computer-implemented method includes creating a wallet for asset tokens and creating an account for a first user upon completion of user onboarding validation. The method includes associating the account with one of an individual and a business entity. The method includes initiating one of know-your-customer (KYC) for the individual and know-your-business (KYB) for the business entity. The method includes enabling the first user to access and make transactions using the wallet.

Abstract: In certain aspects of the disclosure, a computer-implemented method includes creating a wallet for asset tokens and creating an account for a first user upon completion of user onboarding validation. The method includes associating the account with one of an individual and a business entity. The method includes initiating one of know-your-customer (KYC) for the individual and know-your-business (KYB) for the business entity. The method includes enabling the first user to access and make transactions using the wallet.

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.