Abstract: A method of making a multicomponent photocatalyst, includes inducing precipitation from a pre-cursor solution comprising a pre-cursor of a plasmonic material and a pre-cursor of a reactive component to form co-precipitated particles; collecting the co-precipitated particles; and annealing the co-precipitated particles to form the multicomponent photocatalyst comprising a reactive component optically, thermally, or electronically coupled to a plasmonic material.

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 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 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: An infrared photodetector includes: a p-type and highly-doped silicon substrate; a metal structure disposed on the silicon substrate; a first electric contact to the silicon substrate; and a second electric contact to the metal structure.

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: 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: A system, method, and product for determining a reason for a deep learning model output that obtain training data associated with training samples and first labels for the training samples; train a first model using the training samples and the first labels, training the first model generating predictions for the training samples; train a second model using the training samples and the predictions as second labels for the training samples; extract one or more weights of the trained second model; process, using the first model, input data including features associated with at least one sample, to generate output data, the output data including at least one prediction for the at least one sample; and apply the one or more extracted weights to the features to determine one or more contributions of one or more features of the features to the at least one prediction for the at least one sample.

Abstract: Described are a system, method, and computer program product for early detection of and response to a merchant data breach through machine-learning analysis. The method includes receiving transaction data associated with a plurality of transactions and receiving fraudulent transaction data representative of at least one previously identified data-breach incident. The method also includes generating a first model input dataset associated with the at least one merchant and a second model input dataset associated with the at least one previously identified data-breach incident. The method also includes training at least one machine-learning prediction model to associate merchants with a likelihood of data breach and determining at least one breached merchant of the at least one merchant. The method further includes generating a communication configured to cause at least one action to be taken in response to the determination of the at least one breached merchant.

Abstract: Provided is a method that includes determining a plurality of features of a dataset associated with a machine learning model that has been trained, determining a value of at least one feature in each data record of a plurality of data records in the dataset, calculating an average value of the at least one feature in each data record, replacing an original value of the at least one feature in each data record with the average value of the values of the at least one feature in each data record, and determining a metric of model performance of the machine learning model based on the dataset that includes the original value of the at least one feature in each data record replaced with the average value of the values of the at least one feature in each data record. A system and computer program product are also provided.

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: A multicomponent photocatalyst includes a reactive component optically, electronically, or thermally coupled to a plasmonic material. A method of performing a catalytic reaction includes loading a multicomponent photocatalyst including a reactive component optically, electronically, or thermally coupled to a plasmonic material into a reaction chamber; introducing molecular reactants into the reaction chamber; and illuminating the reaction chamber with a light source.

Abstract: A system, method, and product for determining a reason for a deep learning model output that obtain training data associated with training samples and first labels for the training samples; train a first model using the training samples and the first labels, training the first model generating predictions for the training samples; train a second model using the training samples and the predictions as second labels for the training samples; extract one or more weights of the trained second model; process, using the first model, input data including features associated with at least one sample, to generate output data, the output data including at least one prediction for the at least one sample; and apply the one or more extracted weights to the features to determine one or more contributions of one or more features of the features to the at least one prediction for the at least one sample.

Abstract: A method for automatically generating a suggested fraud rule for an issuer includes: identifying a first issuer from a plurality of issuers; determining a transaction volume category and a region category associated with the first issuer; determining a second issuer from the plurality of issuers associated with the same transaction volume category and region category; associating the first issuer and the second issuer to form an issuer consortium; obtaining consortium transaction data associated with the issuer consortium; generating a suggested fraud rule for the first issuer based on the consortium transaction data; and communicating the suggested fraud rule to a first issuer system associated with the first issuer.

Abstract: An infrared photodetector includes: a p-type and highly-doped silicon substrate; a metal structure disposed on the silicon substrate; a first electric contact to the silicon substrate; and a second electric contact to the metal structure.

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: A multicomponent photocatalyst includes a reactive component optically, electronically, or thermally coupled to a plasmonic material. A method of performing a catalytic reaction includes loading a multicomponent photocatalyst including a reactive component optically, electronically, or thermally coupled to a plasmonic material into a reaction chamber, introducing molecular reactants into the reaction chamber, and illuminating the reaction chamber with a light source.

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.