Abstract: A method for detecting fraudulent interactions may include receiving interaction data, including a first plurality of interactions with (first) fraud labels and a second plurality of interactions (without fraud labels). Second fraud label data for each of the second plurality of interactions may be generated with a first neural network (e.g., classifying whether each interaction is fraudulent or not). Generated interaction data and generated fraud label data may be generated with a second neural network. Discrimination data for each of the second plurality of interactions and generated interactions may be generated with a third neural network (e.g., classifying whether the respective interaction is real or not). Error data may be determined based on the discrimination data (e.g., whether the respective interaction is correctly classified). At least one of the neural networks may be trained based on the error data. A system and computer program product are also disclosed.

Abstract: Provided is a system that includes at least one processor programmed or configured to: determine an average payment transaction vector based on a first payment transaction vector associated with a first payment transaction involving an account and a second payment transaction vector associated with a second payment transaction involving the account; determine an account embedding vector associated with the account based on the first payment transaction vector associated with the first payment transaction and the second payment transaction vector associated with the second payment transaction; determine a predicted transaction aggregate vector associated with the account based on the account embedding vector and a plurality of embedding payment transaction vectors associated with a plurality of payment transactions; and store the predicted transaction aggregate vector in a data structure based on an account identifier of the account. A computer-implemented method and computer program product are also provided.

Abstract: Described herein are techniques and systems directed to using context data provided by supplemental devices to reduce false declines during transaction authorization. In some embodiments, upon a transaction being declined based on a level of risk associated with the transaction, a service provider may obtain context data from a number of supplemental devices associated with a user, resource provider, location, or other suitable factor. This context data may then be assessed by the service provider to determine a degree to which the context data supports that the user conducting the transaction is the owner of the account used to conduct the transaction. The level of risk may then be adjusted based on that degree and the adjusted level of risk is used to determine whether to approve or decline the transaction.

Abstract: Provided herein is a computer-implemented method for settling an outstanding invoice issued by a payee, including the steps of capturing a digital image of an invoice issued by a payee to a payor, processing the digital image to identify invoice data and a network location associated with the payee, automatically establishing communication with the network location identified in the digital image, and automatically inputting payment information into one or more fields of the webpage at the network location.

Abstract: Provided herein is a computer-implemented method for settling an outstanding invoice issued by a payee, including the steps of capturing a digital image of an invoice issued by a payee to a payor, processing the digital image to identify invoice data and a network location associated with the payee, automatically establishing communication with the network location identified in the digital image, and automatically inputting payment information into one or more fields of the webpage at the network location.

Abstract: Provided is a computer-implemented method for monitoring and improving data quality of transaction data that may include receiving transaction data associated with a plurality of payment transactions from an acquirer system. The transaction data may include a transaction record associated with each payment transaction of the plurality of payment transactions. Each transaction record may include a plurality of data fields. Each respective data field of the plurality of data fields may be categorized into a respective type of a plurality of types. A data quality score for each respective data field of the plurality of data fields may be determined based on the respective type of the respective data field. A system and computer program product are also provided.

Abstract: Provided is a computer-implemented method for monitoring and improving data quality of transaction data that may include conducting data pre-processing on transaction data associated with a plurality of payment transactions; determining feature values associated with a textual data field in each transaction record of a plurality of transaction records included in the transaction data associated with the plurality of payment transactions, wherein the feature values are used in a parsing layer of a natural language processing (NLP) model after conducting data pre-processing on the transaction data associated with the plurality of payment transactions; and determining whether the feature values associated with the textual data field satisfy one or more rules associated with the parsing layer of the NLP model.

Abstract: Provided is a system, method and computer readable medium for detecting at least one non-compliant commercial card transaction for a plurality of transactions received from a merchant, and for generating at least one score for a received transaction, based at least partially on a scoring model, to determine whether a transaction is non-compliant. The scoring model includes at least one score determined by unsupervised learning with feedback from score influencing rules, case disposition data, transactional data, historical data and old scoring models and automatically modifying, at predefined intervals, the scoring model based on current score influencing rules and case disposition data. Machine learning is programmed to score the model based at least partially on a probability-based outlier detection algorithm and a clustering algorithm and to provide a case presentation system for audit and review of scored transactions and to receive input comprising case disposition data and score influencing rules.