Abstract: A blockchain architecture allows blocks to store both public and private data. The public data may be accessible to any node in the blockchain network, while the private data may be accessible only to nodes specified in an access list, which may be provided when a record is added to the blockchain. When a new record is received, any private fields in the record may be identified and encrypted by a receiving node. The key may then be encrypted and sent with the protected record to other nodes in the access list. These nodes can access the encryption key, decrypt the private fields, and provide a consensus decision to a receiving node. After consensus, the protected record may be added to the blockchain, where the public fields may be freely accessed by any node, and the private fields remain accessible only to nodes in the access list.

Abstract: Generating and applying a prediction model based on blockchain data is disclosed. Based on blockchain data related to historical invoices, a prediction model is generated using machine learning. The prediction model is used to generate a recommendation for a particular entity to obtain a target funding amount by a target funding date. The prediction model is applied to the particular entity's outstanding invoices. Based on blockchain data related to the outstanding invoices, the prediction model determines a predicted cost that the particular entity would incur in obtaining funding, by the target funding date, using each outstanding invoice. A funding type to be used for each invoice is also identified. Invoices are selected based on the predicted costs. An interface presents the recommendation to use the selected invoices to obtain the target funding amount by the target funding date.

Abstract: A blockchain architecture allows blocks to store both public and private data. The public data may be accessible to any node in the blockchain network, while the private data may be accessible only to nodes specified in an access list, which may be provided when a record is added to the blockchain. When a new record is received, any private fields in the record may be identified and encrypted by a receiving node. The key may then be encrypted and sent with the protected record to other nodes in the access list. These nodes can access the encryption key, decrypt the private fields, and provide a consensus decision to a receiving node. After consensus, the protected record may be added to the blockchain, where the public fields may be freely accessed by any node, and the private fields remain accessible only to nodes in the access list.

Abstract: A blockchain architecture allows blocks to store both public and private data. The public data may be accessible to any node in the blockchain network, while the private data may be accessible only to nodes specified in an access list, which may be provided when a record is added to the blockchain. When a new record is received, any private fields in the record may be identified and encrypted by a receiving node. The key may then be encrypted and sent with the protected record to other nodes in the access list. These nodes can access the encryption key, decrypt the private fields, and provide a consensus decision to a receiving node. After consensus, the protected record may be added to the blockchain, where the public fields may be freely accessed by any node, and the private fields remain accessible only to nodes in the access list.

Abstract: Operations include generating proposals for accounts payable and accounts receivable netting across multiple trading partners. A netting proposal system uses a machine learning model to generate netting proposals that are likely to be approved by the respective trading partners. The netting proposal system identifies characteristics of attributes of previously accepted netting proposals. Candidate attributes, with the same characteristics of attributes of previously accepted netting proposals, are selected for a new netting proposal. The netting proposal system further identifies characteristics of attributes of previously rejected netting proposals. Candidate attributes, without the same characteristics of attributes of previously rejected netting proposals, are selected for the new netting proposal. Furthermore, candidate attributes, with the same characteristics of attributes of previously rejected netting proposals, are not selected for the new netting proposal.

Abstract: Integrating a blockchain ledger with an application external to the blockchain ledger is disclosed. A first method determines a state, to be written to a blockchain ledger, based on a contract transaction. A second method determines a state, to be written to the blockchain ledger, based on an adapter transaction. The first method includes an invocation of the second method. The second method verifies whether the adapter transaction includes a particular set of information associated with a particular application. If the adapter transaction includes the particular set of information, then both the contract transaction and the adapter transaction are committed to the blockchain ledger. If the adapter transaction does not include the particular set of information, then neither the contract transaction nor the adapter transaction are committed to the blockchain ledger. The state determined based on the adapter transaction is used to update the particular application.

Abstract: A blockchain architecture allows blocks to store both public and private data. The public data may be accessible to any node in the blockchain network, while the private data may be accessible only to nodes specified in an access list, which may be provided when a record is added to the blockchain. When a new record is received, any private fields in the record may be identified and encrypted by a receiving node. The key may then be encrypted and sent with the protected record to other nodes in the access list. These nodes can access the encryption key, decrypt the private fields, and provide a consensus decision to a receiving node. After consensus, the protected record may be added to the blockchain, where the public fields may be freely accessed by any node, and the private fields remain accessible only to nodes in the access list.

Abstract: Generating and applying a prediction model based on blockchain data is disclosed. Based on blockchain data related to historical invoices, a prediction model is generated using machine learning. The prediction model is used to generate a recommendation for a particular entity to obtain a target funding amount by a target funding date. The prediction model is applied to the particular entity's outstanding invoices. Based on blockchain data related to the outstanding invoices, the prediction model determines a predicted cost that the particular entity would incur in obtaining funding, by the target funding date, using each outstanding invoice. A funding type to be used for each invoice is also identified. Invoices are selected based on the predicted costs. An interface presents the recommendation to use the selected invoices to obtain the target funding amount by the target funding date.

Abstract: Integrating a blockchain ledger with an application external to the blockchain ledger is disclosed. A first method determines a state, to be written to a blockchain ledger, based on a contract transaction. A second method determines a state, to be written to the blockchain ledger, based on an adapter transaction. The first method includes an invocation of the second method. The second method verifies whether the adapter transaction includes a particular set of information associated with a particular application. If the adapter transaction includes the particular set of information, then both the contract transaction and the adapter transaction are committed to the blockchain ledger. If the adapter transaction does not include the particular set of information, then neither the contract transaction nor the adapter transaction are committed to the blockchain ledger. The state determined based on the adapter transaction is used to update the particular application.

Abstract: Operations include generating proposals for accounts payable and accounts receivable netting across multiple trading partners. A netting proposal system uses a machine learning model to generate netting proposals that are likely to be approved by the respective trading partners. The netting proposal system identifies characteristics of attributes of previously accepted netting proposals. Candidate attributes, with the same characteristics of attributes of previously accepted netting proposals, are selected for a new netting proposal. The netting proposal system further identifies characteristics of attributes of previously rejected netting proposals. Candidate attributes, without the same characteristics of attributes of previously rejected netting proposals, are selected for the new netting proposal. Furthermore, candidate attributes, with the same characteristics of attributes of previously rejected netting proposals, are not selected for the new netting proposal.