Abstract: A computer-implemented method and system for visualizing card transaction fraud analysis is presented. Transaction data and account data related to one or more payment card accounts is stored in a database. The transaction data includes a fraud score. A computer processor generates one or more of a plurality of visualizations of activity of at least one suspicious account from the one or more payment card accounts for display in a graphical user interface, each of the plurality of visualizations providing at least a graphical representation of the transaction data and which is selectable from a menu provided by the computer processor in the graphical user interface. The visualizations assist in case judgment of the one or more payment cards.

Abstract: The subject matter disclosed herein provides methods, apparatus, systems, techniques, and articles for false positive reduction in abnormality detection models. A date and time of a first transaction by a transaction entity and associated with a transaction characteristic can be stored. Data representing subsequent transactions associated with the transaction characteristic can be stored. A history marker profile specific to the transaction characteristic and transaction entity can be generated and can include the transaction characteristic, the date and time of the first transaction, and maximum and mean abnormality scores. A date and time of a current transaction can be determined. A current abnormality score for the current transaction can be received. A tenure of the observed transaction characteristic can be computed.

Abstract: A system and method are disclosed, to distinguish fraudulent transactions from a legitimate transaction, predicated on the notion that the card is considered likely to be in state of fraud. The disclosed system and method can be activated as soon as an account has suspicious activity that causes a high score for potential fraud, but before a bank either can or needs to confirm fraud. The system or method is able to pinpoint the actual fraudulent transactions inside a window of potential fraudulent activity, using a specialized model referred to as the pinpoint model.

Abstract: Computer-implemented machines, systems and methods for providing insights about misalignment in a latent space of a machine learning model. A method includes initializing a second weight matrix of a second artificial neural network based on a first weight matrix from a first artificial neural network. The method further includes applying transfer learning between the first artificial neural network and the second artificial neural network. The method further includes comparing the first latent space with the second latent space. The method further includes determining, responsive to the comparing, a first score indicating alignment of the first latent space and the second latent space. The method further includes determining, and responsive to the first score satisfying a threshold, an appropriateness of the machine learning model.

Abstract: A biometric measures profiling analytics system and method are presented. The system and method include collecting biometric data associated with a consumer, and determining one or more biometric variables representing a measurable aspect of the biometric data. The system and method further include generating, based on at least one of the one or more biometric variables, at least one biometric profile variable associated with the consumer, the at least one biometric profile variable representing a degree of normality or abnormality of the collected and calibrated biometric data as compared to a biometric history of the consumer. The system and method further include generating a behavioral score for the consumer based on the collected and calibrated biometric data and with at least one biometric profile variable, the behavioral score representing a degree of risk of normality or abnormality of an event associated with the biometric data.

Abstract: This document presents multi-layered, self-calibrating analytics for detecting fraud in transaction data without substantial historical data. One or more variables from a set of variables are provided to each of a plurality of self-calibrating models that are implemented by one or more data processors, each of the one or more variables being generated from real-time production data related to the transaction data. The one or more variables are processed according to each of the plurality of self-calibrating models implemented by the one or more data processors to produce a self-calibrating model output for each of the plurality of self-calibrating models. The self-calibrating model output from each of the plurality of self-calibrating models is combined in an output model implemented by one or more data processors. Finally, a fraud score output for the real-time production data is generated from the self-calibrating model output.

Abstract: A computer-implemented method for technologically improving a computer-implemented machine-learning model, the method comprising receiving, by a model, at least a first data record; generating a first score representing a first likelihood that the first data record is associated with a first classification, in response to feedback received from one or more data sources communicating with at least one computing system on which the model is implemented; generating a second score to represent a second likelihood that the first data record is associated with the first classification, in response to the first score being higher than a threshold value.

Abstract: The subject matter disclosed herein provides methods, apparatus, systems, techniques, and articles for false positive reduction in abnormality detection models. A date and time of a first transaction by a transaction entity and associated with a transaction characteristic can be stored. Data representing subsequent transactions associated with the transaction characteristic can be stored. A history marker profile specific to the transaction characteristic and transaction entity can be generated and can include the transaction characteristic, the date and time of the first transaction, and maximum and mean abnormality scores. A date and time of a current transaction can be determined. A current abnormality score for the current transaction can be received. A tenure of the observed transaction characteristic can be computed.

Abstract: The subject matter disclosed herein provides methods, apparatus, systems, techniques, and articles for false positive reduction in abnormality detection models. A date and time of a first transaction by a transaction entity and associated with a transaction characteristic can be stored. Data representing subsequent transactions associated with the transaction characteristic can be stored. A history marker profile specific to the transaction characteristic and transaction entity can be generated and can include the transaction characteristic, the date and time of the first transaction, and maximum and mean abnormality scores. A date and time of a current transaction can be determined. A current abnormality score for the current transaction can be received. A tenure of the observed transaction characteristic can be computed.

Abstract: A computer-implemented method includes receiving a new data record associated with a transaction, and generating, using an adaptive model executed by the computer, a score to represent a likelihood that the transaction is associated with fraud. The adaptive model employs feedback from one or more external data sources, the feedback containing information about one or more previous data records associated with fraud and non-fraud by at least one of the one or more external data sources. Further, the adaptive model uses the information about the one or more previous data records as input variables to update scoring parameters used to generate the score for the new data record.

Abstract: A computer-implemented method and system for visualizing card transaction fraud analysis is presented. Transaction data and account data related to one or more payment card accounts is stored in a database. The transaction data includes a fraud score. A computer processor generates one or more of a plurality of visualizations of activity of at least one suspicious account from the one or more payment card accounts for display in a graphical user interface, each of the plurality of visualizations providing at least a graphical representation of the transaction data and which is selectable from a menu provided by the computer processor in the graphical user interface. The visualizations assist in case judgment of the one or more payment cards.

Abstract: A system and method for rapid data investigation and data integrity analysis is disclosed. A data set is received by a server computer from one or more client computers connected with the server computer via a communications network, and the data set is stored in a distributed storage memory. One or more analytical processes are executed on the data set from the distributed storage memory to generate statistics based on each of the analytical processes, and the statistics are stored in a random access memory, the random access memory being accessible by one or more compute nodes, which generate a graphical representation of at least some statistics stored in the random access memory. The graphical representation of at least some statistics is then formatted for transmission to and display by the one or more client computers.

Abstract: A computer-implemented method and system for visualizing card transaction fraud analysis is presented. Transaction data and account data related to one or more payment card accounts is stored in a database. The transaction data includes a fraud score. A computer processor generates one or more of a plurality of visualizations of activity of at least one suspicious account from the one or more payment card accounts for display in a graphical user interface, each of the plurality of visualizations providing at least a graphical representation of the transaction data and which is selectable from a menu provided by the computer processor in the graphical user interface. The visualizations assist in case judgment of the one or more payment cards.

Abstract: A system and method are disclosed, to distinguish fraudulent transactions from a legitimate transaction, predicated on the notion that the card is considered likely to be in state of fraud. The disclosed system and method can be activated as soon as an account has suspicious activity that causes a high score for potential fraud, but before a bank either can or needs to confirm fraud. The system or method is able to pinpoint the actual fraudulent transactions inside a window of potential fraudulent activity, using a specialized model referred to as the pinpoint model.

Abstract: A system and method for detecting a test event involving a financial transaction device at a merchant having a merchant profile is disclosed. The method includes receiving data associated with a transaction involving a financial transaction device; calculating a score using at least the transaction data; comparing the score to a threshold value; and attaching a merchant probe flag to the merchant profile if the score exceeds the threshold value. The merchant probe flag indicates a likelihood that a test event has occurred at the merchant based on the score. If a test event has occurred, then financial transaction devices involved in the test event can have their profiles updated to reflect that they have been probed. If a financial transaction device that has been probed is used in a subsequent transaction, then a specialized fraud scoring model can be used to provide an improved fraud risk score.

Abstract: A system and method for rapid data investigation and data integrity analysis is disclosed. A data set is received by a server computer from one or more client computers connected with the server computer via a communications network, and the data set is stored in a distributed storage memory. One or more analytical processes are executed on the data set from the distributed storage memory to generate statistics based on each of the analytical processes, and the statistics are stored in a random access memory, the random access memory being accessible by one or more compute nodes, which generate a graphical representation of at least some statistics stored in the random access memory. The graphical representation of at least some statistics is then formatted for transmission to and display by the one or more client computers.

Abstract: A computerized method for detecting fraud includes obtaining frequency information on entities in transaction data for at least one individual account, converting frequency information to a frequency variable, and predicting whether an activity is fraudulent in response to the frequency variable. In some embodiments, the frequency variable is used with at least one other variable to predict fraudulent activity.

Abstract: A system and method for rapid data investigation and data integrity analysis is disclosed. A data set is received by a server computer from one or more client computers connected with the server computer via a communications network, and the data set is stored in a distributed storage memory. One or more analytical processes are executed on the data set from the distributed storage memory to generate statistics based on each of the analytical processes, and the statistics are stored in a random access memory, the random access memory being accessible by one or more compute nodes, which generate a graphical representation of at least some statistics stored in the random access memory. The graphical representation of at least some statistics is then formatted for transmission to and display by the one or more client computers.

Abstract: A system and method for multiple funding account payment instrument analytics is disclosed. A payment instrument profile is generated for a payment instrument that characterizes past activity on the payment instrument and past activity across one or more funding accounts associated with the payment instrument. Current activity on the payment instrument and current activity across one or more funding accounts associated with the payment instrument are monitored to detect an abnormality against the payment instrument profile. A fraud score for the current activity on the payment instrument and current activity across one or more funding accounts associated with the payment instrument is generated. The fraud score is based on a quantified extent of the abnormality.

Abstract: A computer-implemented method of fraud detection includes clustering samples on the tree nodes in the decision tree model on the training dataset, calculating the cluster centroids and determining the high fidelity radius for a preset threshold probability for each cluster and determining the left-over class probability for each node. The new transactional data is classified in three steps: first to determine based on the decision tree what leaf node the transaction is associated, second to determine the membership to a cluster of the leaf node using the shortest distance to the cluster centroid and then third to compare the distance with the high fidelity radius and then to determine the eventual class probability for a new data. The new method demonstrates better performance than the decision-tree alone model.