Abstract: A machine-learning algorithm is trained with features relevant to transaction exceptions, distributions of items in transaction mapped to product hierarchies, and operator data. The trained algorithm is trained to predict whether a given transaction requires a transaction exception for potential fraud or for management approval. The trained algorithm is then provided a set of in-progress input data for an in-progress transaction being processed on a transaction terminal. Output from the trained algorithm is used to determine whether the in-progress transaction is allowed to continue processing unabated or whether the in-progress transaction is to be suspended with a transaction exception requiring a manager override or security credential to continue processing.

Abstract: A machine-learning algorithm is trained with features relevant to basket data for items of transactions. The trained algorithm is trained to predict whether a given transaction is more or less likely to be associated with theft being engaged in by a transaction operator for the transaction. The trained algorithm is then provided basket data for a given transaction and produces as output a theft prediction value. When the theft prediction value exceeds a configured threshold value, the transaction is flagged for manual intervention or the transaction is flagged for subsequent manual verification.

Abstract: A machine-learning algorithm is trained with features relevant to a modeled set of input directed to patterns of activities specific to a given behavior. The trained algorithm is also trained on success and failures of remediation actions that change or do not change the given behavior. The trained algorithm is then provided the modeled set of input at predefined intervals of time and supplies as output expected deviations/changes that are predicted for the given behavior along with an indication as to whether the remediation actions are likely to prevent or change the expected behaviors.

Abstract: One method embodiment includes receiving a transaction dataset including data representative of transactions including data representative of at least one product purchased within the respective transactions. This method then processes the dataset according to a contextualizing algorithm to generate a data representation for at least some products included in transactions of the transaction dataset. Each generated data representation represents a context of a product with regard to each of the other products of the data representation. This method further includes processing the generated data representations according to a clustering algorithm to partition products represented by the generated data representations into a number of product clusters. A data representation of the product clusters may then be stored including data identifying products and the product clusters to which they are partitioned.

Abstract: A machine-learning algorithm is trained with features relevant to transaction exceptions, distributions of items in transaction mapped to product hierarchies, and operator data. The trained algorithm is trained to predict whether a given transaction requires a transaction exception for potential fraud or for management approval. The trained algorithm is then provided a set of in-progress input data for an in-progress transaction being processed on a transaction terminal. Output from the trained algorithm is used to determine whether the in-progress transaction is allowed to continue processing unabated or whether the in-progress transaction is to be suspended with a transaction exception requiring a manager override or security credential to continue processing.

Abstract: A machine-learning algorithm is trained with features relevant to basket data for items of transactions. The trained algorithm is trained to predict whether a given transaction is more or less likely to be associated with theft being engaged in by a transaction operator for the transaction. The trained algorithm is then provided basket data for a given transaction and produces as output a theft prediction value. When the theft prediction value exceeds a configured threshold value, the transaction is flagged for manual intervention or the transaction is flagged for subsequent manual verification.

Abstract: A machine-learning algorithm is trained with features relevant to a modeled set of input directed to patterns of activities specific to a given behavior. The trained algorithm is also trained on success and failures of remediation actions that change or do not change the given behavior. The trained algorithm is then provided the modeled set of input at predefined intervals of time and supplies as output expected deviations/changes that are predicted for the given behavior along with an indication as to whether the remediation actions are likely to prevent or change the expected behaviors.

Abstract: Various embodiments herein each include at least one of systems, methods, and software implementing a data-driven classifier. One method embodiment includes receiving a transaction dataset including data representative of transactions including data representative of at least one product purchased within the respective transactions. This method then processes the dataset according to a contextualizing algorithm to generate a data representation for at least some products included in transactions of the transaction dataset. Each generated data representation represents a context of a product with regard to each of the other products of the data representation. This method further includes processing the generated data representations according to a clustering algorithm to partition products represented by the generated data representations into a number of product clusters.

Abstract: A machine-learning algorithm is trained with features relevant to predict a time-series value or rate for a current interval of time. The actual rate is compared against the predicted rate and when a deviation between the actual rate and the predicted rate is outside a threshold deviation, an automated action is processed to attempt to remedy the deviation.

Abstract: Various embodiments herein each include at least one of systems, methods, and software for model-based data validation to identify when self-scan checkout data requires validation. Some embodiments, in the form of a method includes receiving, via a network from a self-scanning device, a self-scan dataset of items for purchase within a purchase data processing transaction and evaluating the self-scan dataset to determine whether to require a rescan of items represented in the self-scan dataset. In such embodiments when a rescan is determined to be required, the method includes transmitting via the network to at least one of the self-scan device and at least one device of a store employee data indicating a rescan is required. However, when a rescan is not determined to be required, the method includes permitting the purchase data processing transaction to proceed.

Abstract: Various embodiments herein each include at least one of systems, methods, and software for model-based data validation to identify when self-scan checkout data requires validation. Some embodiments, in the form of a method includes receiving, via a network from a self-scanning device, a self-scan dataset of items for purchase within a purchase data processing transaction and evaluating the self-scan dataset to determine whether to require a rescan of items represented in the self-scan dataset. In such embodiments when a rescan is determined to be required, the method includes transmitting via the network to at least one of the self-scan device and at least one device of a store employee data indicating a rescan is required. However, when a rescan is not determined to be required, the method includes permitting the purchase data processing transaction to proceed.