Abstract: A fraud prevention server that includes an electronic processor and a memory. The memory includes an online application origination (OAO) service and a plurality of OAO models, each of the plurality of OAO models differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device. When executing the OAO service, the electronic processor is configured to receive form data from a client server, determine a best OAO model from a plurality of OAO models with deep-learning, determine a fraud score of the online application based on the best OAO model, and control the client server to approve, hold, or deny the online application based on the fraud score that is determined.

Abstract: Embodiments provide methods and systems for unsupervised representation learning for bipartite graphs. Method performed by server system includes accessing historical transaction data from database. Method includes generating a bipartite graph based on historical transaction data. Bipartite graph represents a computer-based graph representation of a plurality of cardholders as first nodes and a plurality of merchants as second nodes and payment transactions between first nodes and second nodes as edges. Method includes sampling direct neighbor nodes and skip neighbor nodes associated with a node based on neighborhood sampling method and executing direct neighborhood aggregation method and skip neighborhood aggregation method to obtain direct neighborhood embedding and skip neighborhood embedding associated with node, respectively.

Abstract: Embodiments provide methods and systems for determining optimal interbank network for routing payment transactions. Method performed by server system includes accessing historical transaction data of acquirer from acquirer database, determining payment transaction types corresponding to future payment transactions processing via acquirer for particular period of time based on historical transaction data, predicting fixed interchange cost for each payment transaction type incurring to acquirer for routing future payment transactions through interbank network of interbank networks based on interchange prediction model, performing linear optimization utilizing set of metrics, to make decision whether to apply merchant-specific discount to particular payment transaction type, or not, and routing real-time payment transactions through optimal interbank networks with lowest total transaction cost.

Abstract: A fraud prevention server that includes an electronic processor and a memory. The memory includes an online application origination (OAO) service and a plurality of OAO models, each of the plurality of OAO models differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device. When executing the OAO service, the electronic processor is configured to receive form data from a client server, determine a best OAO model from a plurality of OAO models with deep-learning, determine a fraud score of the online application based on the best OAO model, and control the client server to approve, hold, or deny the online application based on the fraud score that is determined.

Abstract: A computing device for analyzing data from user spending patterns to determine offers to be presented to credit card customers comprises a processing element configured to: receive transaction data for a plurality of transactions for each of a plurality of credit card numbers; input the transaction data into an encoder that performs linear transformations and nonlinear transformations to produce latent space data with each latent space data point being associated with one credit card number; input the latent space data into a clustering element which associates each credit card number with one of a plurality of clusters; and make an upgrade offer to credit card numbers that have a normal credit status and which are associated with clusters that include credit card numbers that have a preferred credit status.

Abstract: The disclosure relates to methods and systems for predicting time of occurrence of future server failures using server logs and a stream of numeric time-series data occurred with a particular time window. Method performed by processor includes accessing plurality of server logs and stream of numeric time-series data, applying density and sequential machine learning model over plurality of server logs for obtaining first and second outputs, respectively, applying a stochastic recurrent neural network model over the stream of time-series data to obtain third output. The method includes aggregating first, second, and third outputs using an ensemble model, predicting likelihood of at least one future server anomaly based on the aggregating, and determining time of occurrence of the at least one future server anomaly by capturing server behavior characteristics using time-series network model. The server behavior characteristics include time-series patterns of the stream of numeric time-series data.

Abstract: Embodiments provide methods and systems for predicting chargeback behavioral data of an account holder. The method performed by a server system includes accessing payment transaction data associated with the account holder from a transaction database. The payment transaction data includes a set of transaction indicators corresponding to payment transactions performed by the account holder within a predetermined time period. The method further includes generating a set of transaction features based on the set of transaction indicators. Furthermore, the method includes computing, via a chargeback risk prediction model, a set of chargeback risk probability scores corresponding to one or more time intervals associated with the account holder based, at least in part, on the set of transaction features. The method also includes transmitting a notification to an issuer server associated with the account holder based, at least in part, on the set of chargeback risk probability scores.

Abstract: Systems and methods are provided for determining an environmental impact of one or more transactions. An example method generally includes accessing transaction data representative of a plurality of transactions, where each of the transactions involves a user and a merchant, and accessing at least one index indicative of environmental impact of a plurality of merchants. The method also includes generating a graph based on the users and merchants included in the transaction data, whereby the graph is representative of the plurality of transactions, and determining a mapping between ones of the merchants involved in the transaction data and the environmental impact indicated by the at least one index, based on at least one of: the graph, the accessed at least one index, a graph convolution network (GCN), and a graph neural network (GNN). The method then includes publishing the mapping between the ones of the merchants and the environmental impact.

Abstract: Embodiments provide methods and systems for determining optimal interbank network for routing payment transactions. Method performed by server system includes accessing historical transaction data of acquirer from acquirer database, determining payment transaction types corresponding to future payment transactions processing via acquirer for particular period of time based on historical transaction data, predicting fixed interchange cost for each payment transaction type incurring to acquirer for routing future payment transactions through interbank network of interbank networks based on interchange prediction model, performing linear optimization utilizing set of metrics, to make decision whether to apply merchant-specific discount to particular payment transaction type, or not, and routing real-time payment transactions through optimal interbank networks with lowest total transaction cost.

Abstract: The disclosure relates to methods and systems for predicting time of occurrence of future server failures using server logs and a stream of numeric time-series data occurred with a particular time window. Method performed by processor includes accessing plurality of server logs and stream of numeric time-series data, applying density and sequential machine learning model over plurality of server logs for obtaining first and second outputs, respectively, applying a stochastic recurrent neural network model over the stream of time-series data to obtain third output. The method includes aggregating first, second, and third outputs using an ensemble model, predicting likelihood of at least one future server anomaly based on the aggregating, and determining time of occurrence of the at least one future server anomaly by capturing server behavior characteristics using time-series network model. The server behavior characteristics include time-series patterns of the stream of numeric time-series data.

Abstract: Embodiments provide methods and systems of predicting server failures. A method may include accessing distinct log clusters representing instructions executed in server, applying first density machine learning model over input vector of distinct log clusters, with length equal to number of distinct log clusters, for obtaining first prediction output, applying first sequential machine learning model over time length sequence of distinct log clusters for obtaining second prediction output, applying second density machine learning model over input vector for obtaining third prediction output, applying second sequential machine learning model over time length sequence of distinct log clusters for obtaining fourth prediction output, aggregating first, second, third and fourth prediction outputs by ensemble model, and predicting likelihood of next log clusters to have anomalous behavior based on the aggregating. First density and first sequential models are trained by normal logs.