Abstract: Recommendations of one or more authorizing components are provided to issuers and/or merchants for enhancing approval rates of payment processing requests. A server system receives a payment authorization request for a payment transaction between a cardholder and a merchant in real time. Payment transaction features associated with the payment transaction are identified based on the payment authorization request. A combination of one or more authorizing components to be applied to the payment transaction is predicted to obtain a product recommendation strategy for the payment transaction. The combination of one or more authorizing components is predicted based on a trained machine learning model and the payment transaction features. The payment authorization request and the product recommendation strategy are transmitted to an issuer associated with the cardholder.

Abstract: A computing device for determining a new credit card number that is a continuation match with an old credit card number of a credit card account that has changed numbers comprises a processing element programmed to: receive transactional data for a plurality of credit card numbers, determine a plurality of old credit card numbers and a plurality of new credit card numbers, determine a plurality of clusters of new credit card numbers, convert the transactional data for each old credit card number and the associated cluster of new credit card numbers into snapshots with an image-like data format, train a modified siamese network with instances of snapshots of an old credit card number, a first new credit card number, and a second new credit card number, and use the modified siamese network to determine one new credit card number that is an upgrade of one old credit card number.

Abstract: A computing device for determining a new credit card number that is a continuation match with an old credit card number of a credit card account that has changed numbers comprises a processing element programmed to: receive transactional data for a plurality of credit card numbers, determine a plurality of old credit card numbers and a plurality of new credit card numbers, determine a plurality of clusters of new credit card numbers, convert the transactional data for each old credit card number and the associated cluster of new credit card numbers into snapshots with an image-like data format, train a modified siamese network with instances of snapshots of an old credit card number, a first new credit card number, and a second new credit card number, and use the modified siamese network to determine one new credit card number that is an upgrade of one old credit card number.

Abstract: A method for improving consensus in a blockchain network through sampling includes: executing, by a processor of a blockchain node in a blockchain network that manages a blockchain, a script to identify a first sample comprised of a subset of nodes of a plurality of nodes in the blockchain network, wherein nodes included in the subset of nodes are randomly selected; performing, by the subset of nodes comprising the first sample, a first consensus operation among all nodes in the subset of nodes to identify a sample consensus block for the blockchain, where each node in the subset of nodes receives the sample consensus block; and repeating, in the blockchain network, executing the script and performing the first consensus operation for a plurality of additional samples, wherein nodes included in the subset of nodes for each additional sample are randomly selected.

Abstract: A computing device for determining a new credit card number that is a continuation match with an old credit card number of a credit card account that has changed numbers comprises a processing element programmed to: receive transactional data for a plurality of credit card numbers, determine a plurality of old credit card numbers and a plurality of new credit card numbers, determine a plurality of clusters of new credit card numbers, convert the transactional data for each old credit card number and the associated cluster of new credit card numbers into snapshots with an image-like data format, train a modified siamese network with instances of snapshots of an old credit card number, a first new credit card number, and a second new credit card number, and use the modified siamese network to determine one new credit card number that is an upgrade of one old credit card number.

Abstract: Embodiments provide methods and systems for reducing decline rates of transaction requests in card-on-file payment transactions. Method performed by server system includes accessing information of a card-on-file payment transaction for a cardholder. The information includes a payment account of the cardholder and a payment amount to be paid to a merchant account of a merchant. Method includes determining a hidden state associated with the cardholder based, at least in part, on a deep Markov model and the payment amount. The deep Markov model is trained based, at least in part, on past customer spending features associated with the cardholder. Method includes predicting a likelihood score of being the card-on-file payment transaction getting approved within a particular time window based, at least in part, on the hidden state associated with the cardholder and providing a notification to the merchant based, at least in part, on the likelihood score.

Abstract: Embodiments provide methods and systems for recommending one or more authorizing components to issuers and/or merchants for enhancing approval rates of payment processing requests. Method performed by server system includes receiving a payment authorization request for a payment transaction between a cardholder and a merchant in real time. The method includes identifying payment transaction features associated with the payment transaction based, at least in part, on the payment authorization request. The method further includes predicting a combination of one or more authorizing components to be applied to the payment transaction to obtain a product recommendation strategy for the payment transaction. The combination of one or more authorizing components is predicted based, at least in part, on a trained machine learning model and the payment transaction features.

Abstract: A method for improving consensus in a blockchain network through decentralized grouping includes: identifying, by each node of a plurality of nodes in a blockchain network that manages a blockchain, a plurality of groups, where each is comprised of a subset of nodes; generating, by each node in each subset of nodes, a new block for the blockchain; performing, by each subset of nodes, a first consensus operation among all nodes in the subset of nodes for the new block generated by in the subset of nodes to identify a group consensus block, where each node in the subset of nodes receives the group's group consensus block; and performing, by the blockchain network, a second consensus operation among all groups for the identified group consensus block to identify an overall consensus block, where a majority of groups of the plurality of groups receives the overall consensus block.

Abstract: Embodiments provide anti-money laundering methods, and systems for detecting potential money laundering financial transactions using artificial intelligence. The method performed by a server system includes receiving data elements associated with financial activities of users who are associated with at least one issuer. The data elements include transaction data associated with users. The method includes identifying graph features based on data elements, and creating temporal knowledge graph based on the graph features. The temporal knowledge graph represents a computer-based graph representation of the users as nodes and relations among the nodes as edges.

Abstract: A method for improving consensus in a blockchain network through decentralized grouping includes: identifying, by each node of a plurality of nodes in a blockchain network that manages a blockchain, a plurality of groups, where each is comprised of a subset of nodes; generating, by each node in each subset of nodes, a new block for the blockchain; performing, by each subset of nodes, a first consensus operation among all nodes in the subset of nodes for the new block generated by in the subset of nodes to identify a group consensus block, where each node in the subset of nodes receives the group's group consensus block; and performing, by the blockchain network, a second consensus operation among all groups for the identified group consensus block to identify an overall consensus block, where a majority of groups of the plurality of groups receives the overall consensus block.

Abstract: A method for improving consensus in a blockchain network through sampling includes: executing, by a processor of a blockchain node in a blockchain network that manages a blockchain, a script to identify a first sample comprised of a subset of nodes of a plurality of nodes in the blockchain network, wherein nodes included in the subset of nodes are randomly selected; performing, by the subset of nodes comprising the first sample, a first consensus operation among all nodes in the subset of nodes to identify a sample consensus block for the blockchain, where each node in the subset of nodes receives the sample consensus block; and repeating, in the blockchain network, executing the script and performing the first consensus operation for a plurality of additional samples, wherein nodes included in the subset of nodes for each additional sample are randomly selected.