A transaction processing system is arranged to receive, from a user device, transaction data and outcome data associated with outcomes of one or more previous transactions. Based on the transaction data and the outcome data, the transaction processing system may process the transaction request according to a particular transaction process and update the outcome data. The updated outcome data may be sent to the user device and stored for use in processing a future transaction.

Systems and methods for issuer-specified domain control on a payment instrument are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for issuer-specified domain controls on a payment instrument may include: (1) receiving, from an issuer of a financial instrument, an identification of a domain control or restriction on a payment token for the financial instrument, the domain control or restriction to be applied by the issuer; (2) requesting generation of the payment token with a pass-through indicator from a token service provider; (3) receiving, from the token service provider, the payment token; and (4) storing an association of the domain control or restriction and the payment token.

Various embodiments are generally directed to techniques for generating rules automatically navigate a website and perform updates of payment token information.

A computer-implemented method includes receiving, from a mobile device, a deposit request including a first check image for funds to be deposited into a user account, determining a risk level associated with the deposit request, requesting a second check image having a restricted endorsement based on the risk level, receiving the second check image having the restricted endorsement, determining a confidence level based on the restricted endorsement, and transferring the funds to the user account based on the confidence level.

A system and method for implementing a fast payment is described. A request to execute a payout to a payee account is received, where the request includes information about the payout. At least one payment rail available for the requested payout is determined from a plurality of payment rails based on the included information. Rules associated with each of the at least one payment rail are identified. A fastest payment rail is determined from the at least one payment rail for executing the payout based on the identified rules and the information about the payout. The payout is then executed over the determined fastest payment rail.

A hardware security module (HSM) generates a client key for an account holder of a cryptoasset custodial system. The HSM encrypts the client key to generate an encrypted client key using a hardware-based cryptographic key within a secure storage device. The encrypted client key is transmitted to client devices. The HSM deletes the encrypted client key from the secure storage device. Each client device stores the encrypted client key in an offline secure enclave. A request to authorize a cryptoasset transaction is received. The HSM determines that signed messages endorsing the cryptoasset transaction have been received from at least some client devices in satisfaction of a quorum. The encrypted client key is received from at least one client device. The HSM decrypts the encrypted client key. The HSM signs an approval message for the cryptoasset transaction using a cryptoasset key based at least in part on the client key.

Image analysis is performed on a user at a transaction terminal. Based on behaviors, expressions, and activities of the user, fraud or potential fraud is flagged. When fraud is flagged, the transaction terminal stops processing an active transaction on behalf of the user and alerts are sent. When potential fraud is flagged, images/video associated with the active transaction are sent for review and the active transaction may be suspended or permitted to proceed at the transaction terminal. In an embodiment, a same user conducting multiple transactions with different accounts at a same transaction terminal or multiple different transaction terminals within a configured period of time is automatically identified as fraud based on a fraud rule.

A method includes receiving a payment request that indicates a card identifier corresponding to a payment instrument to be used for payment. The method further includes determining that the card identifiers fails to satisfy at least one card activity criteria. Additionally, method includes subsequent to determining that no failed authorization attempts were performed for the card identifier within a previous time period, retrieving, from a database, a decline probability score associated with the card identifier. The method also includes based on the decline probability score, determining whether to transmit an authorization request for the card identifier prior to processing the payment request.

A transaction classification system obtains a data set including first data associated with a first characteristic and second data associated with a second characteristic. In response to obtaining the data set, the system uses a classification model to generate a classification by classifying the first data into majority data and the second data into minority data. From the classification and using the classification model, the system determines a cost. The system modifies the classification model based on this cost to generate an updated classification model. The system uses the updated classification model to re-classify a subset of the first data into the minority data and a subset of the second data into the majority data.

Methods and systems are presented for configuring a risk assessment engine to predict a risk of a user based on a topic classification across a set of unknown topics generated by a topic model. A risk determination system obtains a plurality of transactions previously conducted by a user. The risk determination system generates a risk document comprising a sequence of words that describe the plurality of transactions. A topic model is used to determine a topic classification for the user based on the sequence of words. The topic model comprises a natural language processor configured to classify the risk document to one or more topics based on the words within the risk document. The risk determination system configures the risk assessment engine to accept the topic classification as input value(s) for predicting a risk associated with the user.