This disclosure describes, in part, techniques for automating transfers of funds from a deposit account of a user to a savings account of the user based on historical transaction data associated with the deposit account. Using historical deposits and withdrawals from the deposit account, a prediction may be made regarding deposits to, and withdrawals from, the deposit account that will occur in a future period. Based on that that information and a balance to maintain in the deposit account for the duration of the future period, the amount of funds to transfer from the deposit account to the savings account may be determined. The techniques may further include transferring the funds to the savings account once determined.

An integration system for a system of platforms includes an orchestration platform, a blockchain transactional platform, a digital transactional platform, a merchant system, a user trust platform, and one or more user devices connected to each other and a distributed ledger and/or a secondary mesh network via one or more networks. The blockchain transactional platform performs accesses and performs actions on the distributed ledger and/or the secondary mesh network. The digital transactional platform maintains transactional data indicative of an amount of first-domain value correlated to a user. The blockchain transactional platform maintains blockchain transactional data indicative of an amount of second-domain value correlated to the user. The orchestration platform manages data exchange, synthesis, fusion, analysis, and transformation between the components of the system, including the orchestration of credit terms using currency balances.

A method for employing user-level credit decisions in relation to extension of a loan to a user may include receiving prequalification information associated with a user and employing a first credit extension model to execute a first credit extension decision based on the prequalification information, where the first credit extension decision is associated with a first credit limit. The method may include employing a second credit extension model, different from the first credit extension model, to execute a second credit extension decision based on the prequalification information and relationship information associated with current and prior interactions between the user and a lending entity making the first credit extension decision and the second credit extension decision, where the second credit extension decision is associated with a second credit limit. The method may also include determining, based on the first and second credit extension decisions, whether to extend a predefined credit limit to the user.

The disclosure relates to non-blocking messaging in an electronic trading venue (ETV) in which a matching engine sends groups of price-compatible contra-orders to a credit engine. Doing so may improve the ETV's efficacy and performance by eliminating the need for multiple roundtrips of messages in the case of a failed credit check, and/or by allowing the ETV to more faithfully implement its matching rules. Upon sending an order to the credit engine the matching engine marks it as ‘uncertain’ to indicate that additional requests received on it (e.g., cancels) must also be forwarded to the credit engine. When all pending requests on an order have been acknowledged by the credit engine the matching engine unmarks it as such. In addition to credit checking the credit engine may also perform computations related to advanced order types (e.g., icebergs) indicating the output of such in its acknowledgements to the matching engine.

A cryptocurrency secured by one or more loans is disclosed. The cryptocurrency is associated with one or more loans and the loan payments are distributed to the owner of the cryptocurrency. An identification of a pool of one or more bundled loans is received, and an indication of payments of the one or more bundled loans in the pool is be received. User information associated with a cryptocurrency exchange may be received, and the cryptocurrency is associated with the pool of one or more bundled loans. Transfer is made, to a user, of an amount of the payments of the one or more bundled loans in the pool.

In some implementations, a credit decision platform may receive a credit request from an applicant and obtain domestic historical data associated with the applicant from a credit bureau device. The credit decision platform may obtain access to an email account associated with the applicant based on determining that the domestic historical data associated with the applicant is insufficient to process the credit request. The credit decision platform may identify, using one or more machine learning models, a set of email messages included in the email account that are relevant to the credit request and may analyze content included in the set of email messages to generate non-domestic historical data associated with the applicant. The credit decision platform may generate a decision on the credit request based on an estimated creditworthiness of the applicant, which may be determined based on the non-domestic historical data.

A federated-data-room server manages information about a collection of electronic documents residing elsewhere (under different organizational/customer control). The server can anchor documents to a blockchain, record usage history of each document, and provide access to the documents for authorized users. As a result, the federated-data-room server operates on customers' data, while leaving the data in control of the customers. At the same time, the federated-data-room server provides data access and enables traceability via blockchain recordation of document identifiers and document hash values.

A method of rating credit risk is provided. The method comprises calculating a number of credit risk factors associated with a financial instrument, wherein each credit risk factor is calculated iteratively at a first timestep as a discrete probabilistic wave function representing a superposition state of scores. The discrete probabilistic wave function of each credit risk factor is measured after each calculation iteration for the first timestep. The probabilistic wave functions of the credit risk factors are then linearly combined to calculate a discrete probabilistic wave function for a final credit rating of the financial instrument for the first timestep, which is displayed in a user interface. The above steps are repeated for a second timestep using the probabilistic wave functions of the credit risk factors at the first timestep as initial states for the second timestep.

The purpose of the present disclosure is to enhance the degree of freedom for settlement, even once the settlement method has been selected by the user. A method for processing information makes an information processing device execute: acquiring information from a unit storing settlement history information including at least one piece of settlement information on each of a plurality of users, the acquired information containing at least one piece of settlement information on a first user; specifying at least one piece of settlement information from the settlement history information, in response to operation by the first user with another information processing device, on which the settlement history information is displayed; displaying an amount of money based on a settlement amount included in the at least one piece of settlement information on the other information processing device, the displayed amount being an amount to be loaned to the first user; and lending the amount of money determined in response to the operation by the first user with the other information processing device to the first user.

Improvements to existing technologies associated with point-of-sale transactions and merchant ecosystems to, among other things, reduce in-person contact and, in some examples, improve the efficiency at which point-of-sale transactions are completed (i.e., reduce friction) are described. In some examples, such reduced in-person contact and/or improved efficiencies can limit transmission of infectious diseases. As such, techniques described are directed to modifying aspects of point-of-sale transactions such that they occur on different computing devices (e.g., customer computing devices instead of merchant computing devices), are automated, and/or occur at different times than with conventional point-of-sale transactions. Furthermore, in at least one example, techniques described can leverage a distributed, network-based merchant ecosystem—comprising multiple merchant computing devices and/or customer computing devices that are specially configured to communicate with a service provider—to facilitate social distancing, which can reduce in-person contact and, in some examples, improve the efficiency at which point-of-sale transactions are completed.