Ncr Voyix Corporation

Shiran Abadi

Tamar Miriam Haizler

Itamar David Laserson

Amit Botzer

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.

Bias detection technique in a data-driven model for multiple tenants

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.

Data-driven transaction terminal item code corrector

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.

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Tel Aviv, Israel

Shiran Abadi