Abstract: Risky actions versus non-risky actions in a transaction are identified and a fraud score associated with probabilities of the risky actions is updated accordingly for purposes of determining whether the transaction is likely or not likely to be associated with fraud. A machine-learning model is trained to predict the risky actions versus non-risky actions of a transaction based on the transaction features as a whole and compare the predicted action labels of risky and non-risky versus the actual actions taken in the transaction to calculate probabilities of risky actions taken and output a risk or fraud score based thereon. Higher probabilities correlate with lower risk scores and vice versus.

Abstract: A first dataset associated with input data provided to a data-driven machine-learning model (MLM) that provides a service to a first tenant is obtained. A second dataset is obtained for a second tenant. Each set of input data from the datasets are labeled with an identifier for the first tenant or the second tenant. The first dataset and second dataset are aggregated into a training dataset to train a classification MLM to classify each set of input data as originating from the first tenant or the second tenant. The classification MLM is tested for accuracy. Based on the accuracy of the classification MLM, a determination is provided as to whether the data-driven MLM can be used with the second tenant without adjustments to the data-driven MLM or as to whether adjustments to the data-driven MLM are needed before using the data-driven MLM.

Abstract: Metrics are captured from a variety of systems associated with stores of a retailer. Values for factors or benchmarks are calculated per store from their corresponding metrics. Each of the stores are labeled as successful or unsuccessful. Factors for which high values are correlated with successful stores and low values are correlated with unsuccessful stores are clustered together. Similarly, factors for which low values are correlated with successful stores and high values are correlated with unsuccessful stores are clustered together. A set of clustered factors associated with the success, or the failure of stores are reported to the retailer in a data model that also comprises the various degrees to which the various clusters of the factors relate to or correlate with both the successful stores and the unsuccessful stores. Prescriptive recommendations are derived from the data model to improve metrics associated with successful factors.

Abstract: A prescriptive data model for stores of a retailer is maintained. The data model comprises clusters of benchmarks and benchmark values for successful stores and unsuccessful stores. A machine-learning model (MLM) is trained on the data model to predict Key Performance Indicator (KPI) values. An interface is provided that permits an end user to override a given current benchmark value with a changed value. The changed value along with unchanged current benchmark values are provided as input to the MLM and the MLM produces as output a set of current predicted KPI values. The set of current predicted KPI is rendered within the interface to the end user as a predicted impact the changed value will have on a given store or a given department of the given store.

Abstract: Risky actions versus non-risky actions in a transaction are identified and a fraud score associated with probabilities of the risky actions is updated accordingly for purposes of determining whether the transaction is likely or not likely to be associated with fraud. A machine-learning model is trained to predict the risky actions versus non-risky actions of a transaction based on the transaction features as a whole and compare the predicted action labels of risky and non-risky versus the actual actions taken in the transaction to calculate probabilities of risky actions taken and output a risk or fraud score based thereon. Higher probabilities correlate with lower risk scores and vice versus.

Abstract: An unrecognized item code is identified during a transaction at a transaction terminal. When the unrecognized code is determined to be associated with a typographical error, alternative corrected item codes are supplied to the transaction terminal to replace the unrecognized code. When the unrecognized code is determined to be missing from a product catalogue, an ordered list of most likely corrected item codes for the unrecognized code is provided to the transaction terminal for selection of one of the corrected item codes by an operator.

Abstract: The probabilities of transitioning between item states for a given item sequence of a given transaction are calculated and item non-fraud scores are calculated from the probabilities for each item of the given transaction. The item non-fraud scores for the items of the transaction are provided to a fraud-detection system for determining whether any of the item non-fraud scores is more likely or less likely to be associated with sweethearting fraud by a cashier that performed the transaction.

Abstract: The probabilities of transitioning between states of a given transaction sequence for a given transaction are calculated and a non-fraud score is calculated from the probabilities. The non-fraud score is provided to a fraud-detection system for determining whether the transaction sequence is more likely or less likely to be associated with fraud.