Abstract: Provided are systems and methods for identifying unclaimed sources of funds such as employers, gig opportunities, businesses, and the like. The process can be used as part of a larger process that may also include fraud checks, deduplication of data, verification of users, analytical insight, and the like. In one example, a method may include establishing a communication channel with a third-party data source via an application programming interface (API), ingesting data records of the user from the third-party data source via the established communication channel based on an account identifier, identifying an unclaimed source of income based on a data value stored within the ingested data records, and displaying an identifier of the unclaimed source of income and an input mechanism which is configured to confirm the identified unclaimed source of income.

Abstract: The present invention generally relates to the use of a stable value digital asset to pay dividends for securities and other financial instruments tied to a blockchain.

Abstract: The present invention generally relates to a method, system and program product for depositing and withdrawing a stable value digital asset tied to a blockchain in exchange for fiat.

Abstract: Systems, methods, and program products for providing exchanges for converting from, to, or between digital assets, and in particular digital math-based assets, such as bitcoins, Namecoins, Litecoins, PPCoins, Tonal bitcoins, IxCoins, Devcoins, Freicoins, I0coins, Terracoins, Liquidcoins, BBQcoins, BitBars, PhenixCoins, Ripple, Dogecoins, Mastercoins, BlackCoins, Ether, Nxt, BitShares-PTS, Quark, Primecoin, Feathercoin, and Peercoin, to name a few, are disclosed. In embodiments, such systems, methods, and program products can further provide or be used in conjunction with automated transactions, digital asset arbitrage systems, and/or kiosk systems for transacting or interacting with digital math-based assets. A kiosk for transacting with digital assets and digital asset exchanges is also disclosed. Systems, methods, and program products for automated transactions and automated digital asset arbitrage transactions are also disclosed.

Abstract: Systems, methods, and program products for providing exchanges for converting from, to, or between digital assets, and in particular digital math-based assets, such as bitcoins, Namecoins, Litecoins, PPCoins, Tonal bitcoins, IxCoins, Devcoins, Freicoins, I0coins, Terracoins, Liquidcoins, BBQcoins, BitBars, PhenixCoins, Ripple, Dogecoins, Mastercoins, BlackCoins, Ether, Nxt, BitShares-PTS, Quark, Primecoin, Feathercoin, and Peercoin, to name a few, are disclosed. In embodiments, such systems, methods, and program products can further provide or be used in conjunction with automated transactions, digital asset arbitrage systems, and/or kiosk systems for transacting or interacting with digital math-based assets. A kiosk for transacting with digital assets and digital asset exchanges is also disclosed. Systems, methods, and program products for automated transactions and automated digital asset arbitrage transactions are also disclosed.

Abstract: Systems and methods for securely storing digital assets using a secure portal are disclosed. Using an isolated computer within an electronic isolation chamber, a plurality of digital asset accounts may be generated, and one or more private keys and a digital asset account identifier corresponding to each of the digital asset accounts may be obtained. A respective reference identifier may be associated with each digital asset account. At least one of the one or more private keys corresponding to each digital asset account may be divided into a plurality of private key segments and written to a card along with the respective reference identifier to create sets of collated cards, wherein each set comprises cards corresponding to different private keys.

Abstract: The present invention generally relates to a method, system and program product for generating a stable value digital asset tied to a blockchain.

Abstract: Provided are systems and methods for verifying an identity of a user based on a data mesh created from various sources of truth. In one example, a method may include establishing, via a host platform, a first and a authenticated communication channel between a host server of a user account and a host server of a second user account, retrieving, via the first and second authenticated communication channels, PII of the user from the first and second user accounts and combining the PII into a meshed data set, determining a difference between the PII within the meshed data set, and verify an identity of the user based on the determined difference between the PII within the meshed data set and transmitting the verification to a computer system.

Abstract: Provided are systems and methods for reconciling transactions from different financial accounts via machine learning. In one example, a method may include storing a data file comprising a plurality of electronic transactions and a plurality of transaction records corresponding to the plurality of electronic transactions in memory, inputting the data file into a first machine learning model and identifying respective transaction attributes of the plurality of transaction records based on the input data file, determining, via execution of a second machine learning model, that a first transaction and a second transaction in the data file correspond to a common transaction based on attributes of the first and second transactions which are identified from respective transactions records of the first and second transactions, and modifying the data file to indicate that the first and second transactions correspond to the common transaction and storing the modified data file in the memory.

Abstract: Provided are systems and methods which can use machine learning to draw additional inferences about transaction records from transaction strings. The inferred data can be used to build a classification model configured to map transaction string to predefined categories. In one example, a method may include receiving a file comprising transaction strings corresponding to a plurality of transaction records, executing the machine learning model on the transaction strings to identify a plurality of categories associated with the transaction strings, generating a classifier model that comprises patterns of keywords from the transactions strings mapped to the plurality of identified categories, respectively, and storing the classifier model in the data store.

Abstract: Provided are systems and methods for identifying a counterpart entity of a transaction from a transaction string. In one example, a method may include identifying a transaction string from an account of a target entity, the transaction string comprising a record of a payment that changed a balance of the account, executing a machine learning model based on the transaction string to determine a counterpart entity of the transaction with respect to the target entity, wherein, during execution, the machine learning model receives the transaction string as input and outputs an identifier of the counterpart entity, generating a data structure comprising a first field that stores the transaction string and a second field that stores an identifier of the counterpart entity, and storing the data structure within a file.

Abstract: The present invention relates to a method, system, and program product for depositing, holding and/or distributing collateral in the form of a stable value token for a security token, the tokens being on the same underlying blockchain. Furthermore, the present invention relates to methods, systems, and program products for lending digital assets, such as crypto currency and other related products.

Abstract: The present invention generally relates to a method, system and program product for modifying a supply of stable value digital asset tokens tied to a blockchain.

Abstract: A trust computer system comprises one or more processors; and non-transitory computer readable memory having stored thereon machine-readable instructions that, when executed by the one or more processors, cause the one or more processors to perform a method comprising: receiving a request for a blended digital math-based asset price for a basket of a plurality of digital math-based assets; requesting electronic digital math-based asset pricing data; receiving the electronic digital math-based asset pricing data; calculating a blended digital math-based asset price; storing the blended digital math-based asset price; accessing a plurality of prior blended digital math-based asset prices; aggregating the blended digital math-based asset price with the plurality of prior blended digital math-based asset prices; and applying the digital math-based asset price index for a benchmark for an instrument.

Abstract: The present invention generally relates to a method, system and program product for modifying a supply of stable value digital asset tokens tied to a blockchain.

Abstract: Autonomous devices and systems, methods, and program products for authorizing and performing autonomous devices transactions are disclosed. An autonomous device can be configured to generate a first hash value of a chain of hash values by applying a hash algorithm to first data including first new data and a first previous hash value of the chain of hash values, the first previous hash value computed by applying the hash algorithm to first previous data. The device can transmit to a transaction computer system the first hash value and the first new data. The device can generate and transmit to the transaction computer system a first signed electronic transaction request comprising first transaction data comprising a sending account identifier associated with the autonomous device, a destination account identifier, a transaction amount, and a timestamp. The device can digitally sign the transaction request using a private key of an asymmetric key pair.

Abstract: The present invention generally relates to a method, system and program product for depositing, holding and/or distributing collateral in the form of digital assets in a peer-to-peer network.

Abstract: The present invention generally relates to the use of stable value digital assets and/or fiat-backed digital assets as cryptocurrencies that can be linked to other digital assets using blockchain technology and/or through a peer-to-peer network. In embodiments, the present invention relates to specific applications of non-fungible digital assets tied to a peer-to-peer network, such as a blockchain network.

Abstract: An exemplary method includes generating, by a trust computer system, script instructions to carry out a transaction involving one or more digital wallets held in a trust custody account so as to verify control of digital assets held in the one or more digital wallets, the trust computer system being operatively connected to a decentralized digital asset network that uses a decentralized electronic ledger in the form of a blockchain maintained by a plurality of computer systems to track asset ownership and/or transactions in a digital asset system, and generating script instructions includes accessing a statement associated with an event that occurred within a predetermined time frame, and generating, by the trust computer system, based on the script instructions, a transaction that involves a zero net payment from the account along with the statement, and payment of a transaction fee from a separate operating account.

Abstract: Provided are systems and methods that rely on machine learning to recommend employment opportunities. In one example, a method may include identifying, via execution of a first machine learning model, income data and spending data of a user, identifying, via execution of a second machine learning model, skill attributes of the user, determining, via execution of a third machine learning model, a recommended job for the user, where the determining comprises inputting the outputs from the first and second machine learning models into the third machine learning model, and displaying, via a user interface, a description of the recommended job.