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.

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 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 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 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 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 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 adhering to currency preferences between counter parties and minimizing currency volatility impacts when settling digital asset transactions. In general, currency conversions between crypto, stablecoin, and fiat are managed at digital platforms functioning as intermediaries between different parties to a transaction, such as, for example, a transaction transferring ownership of a digital asset in exchange for currency. A digital platform can handle currency conversions (e.g., by referring to currency pairs), mitigate risks of the transacting parties, etc. thereby increasing comfort of parties using the digital platform. Different parties may desire to settle transactions in different currencies. The digital platform can obscure currency types being used from counterparties. Stablecoin can be used as an intermediate currency type to reduce potential adverse financial impacts from crypto volatility.

Abstract: The present invention extends to methods, systems, and computer program products for detecting events from ingested data. Data is ingested. A first score is computed from a portion of the data. The first score indicates a likelihood of the data including event information. Computing resources are allocated to further process the data based on the first score indicating that further processing is warranted. A second score is computed from parameters of the data. An event is detected based on the second score.