Systems and methods are described herein for learning an entity’s trust model and risk tolerance. An entity’s trust score may be calculated based on data from a variety of data sources, and this data may be combined according to a set of weights which reflect an entity’s trust model and risk tolerance. For example, an entity may weight data of a certain type more heavily for certain types of transactions and another type of data more heavily for other transactions. By gathering data about the entity, a system may predict the entity’s trust model and risk tolerance and adjust the set of weights accordingly for calculating trust scores. Furthermore, by monitoring how entities adjust weights for different transaction types, default weighting profiles may be created that are customized for specific transaction types. As another example, an entity’s trust score, as reported to a requesting entity, may be adjusted based on that requesting entity’s own trust model, or how “trusting” the requesting entity is.

Systems and methods are described herein for learning an entity’s trust model and risk tolerance. An entity’s trust score may be calculated based on data from a variety of data sources, and this data may be combined according to a set of weights which reflect an entity’s trust model and risk tolerance. For example, an entity may weight data of a certain type more heavily for certain types of transactions and another type of data more heavily for other transactions. By gathering data about the entity, a system may predict the entity’s trust model and risk tolerance and adjust the set of weights accordingly for calculating trust scores. Furthermore, by monitoring how entities adjust weights for different transaction types, default weighting profiles may be created that are customized for specific transaction types. As another example, an entity’s trust score, as reported to a requesting entity, may be adjusted based on that requesting entity’s own trust model, or how “trusting” the requesting entity is.

A data processing system communicates with a secure third-party database to obtain information about a user that is usable to determine one or more items associated with the user. The system then coordinates gathering and identification of additional data relevant to the user from other third-party data sources, to potentially update the user's information stored with the secure third-party database. The updated information may then be accessed at the secure third-party database to determine items associated with the user, which may include additional items in view of the additional data

Methods and systems for automatically conducting a continuous forward rate agreement in a cryptocurrency using smart contracts. An obligation object is generated and provided with control of a first smart contract, which may be a collateralized debt position smart contract, and control of a lender amount. The obligation object is executed to update balances for first and second parties until the obligation object is liquidated or terminated. Additional parties may be introduced. Upon liquidation or termination, the obligation object accounts to each of the parties based on their balances, and based on tokens generated to track positions in the obligation object.

A web based finance system allows users to refinance past eligible purchasing transactions. The system can include a system server(s) which is in communication through a network with client computing devices associated with customers. The server can receive customer information and determine a refinancing value range based upon the user has a bank account and/or credit risk rating information. The server can identify prior eligible credit card purchases for refinancing and the customer can select one or more purchases for refinancing and the refinancing loan terms.

A non-transitory computer-readable recording medium storing a risk analysis program for an artificial intelligence (AI) system, the analysis program being a program for causing a computer to execute processing, the processing including: acquiring a plurality of pieces of relational information that include at least two attributes among an attribute of a type of an object person, an attribute of a type of processing, and an attribute of a type of data, wherein the relational information is determined on a basis of a configuration of the AI system; determining a priority of the plurality of pieces of relational information on a basis of the attribute of the type of the object person; and outputting one or a plurality of check items selected on a basis of the determined priority from among a plurality of check items associated with each attribute as a checklist for the AI system.

A non-transitory computer-readable recording medium storing a risk analysis program for an artificial intelligence (AI) system, the analysis program being a program for causing a computer to execute processing, the processing including: acquiring a plurality of pieces of relational information that include at least two attributes among an attribute of a type of an object person, an attribute of a type of processing, and an attribute of a type of data, wherein the relational information is determined on a basis of a configuration of the AI system; determining a priority of the plurality of pieces of relational information on a basis of the attribute of the type of the object person; and outputting one or a plurality of check items selected on a basis of the determined priority from among a plurality of check items associated with each attribute as a checklist for the AI system.

Disclosed are techniques and an apparatus for accessing a smart contract library that may include a number of templates of different legal contracts implementable as a smart contract between respective parties. Each template may include a number of sections having different contractual terms and conditions and fillable fields for specific contract terms. Each respective section of the number of sections includes programming code operable to enforce conformance with specific section-related contractual terms and conditions of the respective section and with any specific contract terms input to a fillable field of the respective section. A first user address generated based on cryptographic keys may be associated with a contract-creating computing device in a private blockchain. The first user address may be associated with the first user in the private blockchain. A finalized smart contract may have a smart contract address generated from the first user address and a nonce value.

Techniques for risk evaluation include receiving, from a requesting entity, a request for monitoring target entities specifying a first identifier associated with each target entity and target entity information. The system generates a second identifier and a third identifier for each target entity and stores a mapping of the second identifiers to the first identifiers and the third identifiers, preventing the second identifiers from being provided to the requesting entity. The system monitors a periodically updated data set and determines risk metrics for the target entities, comparing each risk metric to a threshold value to identify target entities whose risk data indicates an insider threat. The system generates a third identifier for the identified target entities and provides the third identifiers to the requesting entity. Responsive to a request for a corresponding first identifier, the system identifies and provides the first and third identifiers to the requesting entity.

The technology disclosed relates to creation and operation of a marketplace among trusted small and medium sized banks for participation in commercial loans too large for an individual participating bank to undertake on its own. This technology can be applied to formation of other marketplaces in which trust matters and controlled dissemination of opportunity information, as in layers or rounds, proceeds manually. Aspects of the technology disclosed can be used by initiators and participants to syndicate loans once a marketplace is established, whether the market is established applying the technology disclosed or otherwise. This technology also can be applied to syndicate other opportunities or to resell participations in syndicated loans that have closed and been funded.