Abstract: The present disclosure relates to systems and methods that use an artificial intelligence (AI) model to generate outputs that can be evaluated to predict which logged entry items match entry request record line items of an entry request record. Additionally, the present disclosure relates to systems and methods for intelligently detecting anomalies within data sets.

Abstract: The present disclosure relates to systems and methods that use an artificial intelligence (AI) model to generate outputs that can be evaluated to predict which logged entry items match entry request record line items of an entry request record. Additionally, the present disclosure relates to systems and methods for intelligently detecting anomalies within data sets.

Abstract: Systems and methods described herein relate to determining whether to provide auto-completed values for fields in a digital form. More specifically, for a given field in the digital form, a machine-learning model can be trained to transform an input data set into a predicted field value and can further generate a corresponding confidence metric. A relative-loss parameter can be determined for the field, where the relative-loss parameter represents a loss of responding to an inaccurate predicted field value for the field relative to a loss corresponding to a human user providing a field value for the field. A confidence-metric threshold can be determined for the field based on the relative-loss parameter. For a given usage of the digital form, it can then be determined whether to auto-complete the field with a predicted field value generated by the model by determining whether the corresponding confidence metric exceeds the confidence-metric threshold.

Abstract: The present disclosure relates to systems and methods that use an artificial intelligence (AI) model to generate outputs that can be evaluated to predict which logged entry items match entry request record line items of an entry request record. Additionally, the present disclosure relates to systems and methods for intelligently detecting anomalies within data sets.

Abstract: The present disclosure relates to systems and methods for providing an interface that displays a prediction of remaining code segments of a code comprised of a sequence of code segments. The remaining code segments may be automatically predicted in response to the interface receiving a user's input of at least a portion of a code segment (or a user input of other data elements that are not code segments). Predicting the remaining code segments may be performed using a trained machine-learning model that can generate output(s) predictive of remaining code segments in response to a user inputting at least one code segment of a code into an input element of the interface.

Abstract: The present disclosure relates to systems and methods for providing an interface that displays a prediction of remaining code segments of a code comprised of a sequence of code segments. The remaining code segments may be automatically predicted in response to the interface receiving a user's input of at least a portion of a code segment (or a user input of other data elements that are not code segments). Predicting the remaining code segments may be performed using a trained machine-learning model that can generate output(s) predictive of remaining code segments in response to a user inputting at least one code segment of a code into an input element of the interface.

Abstract: The present disclosure relates to systems and methods that use an artificial intelligence (AI) model to generate outputs that can be evaluated to predict which logged entry items match entry request record line items of an entry request record. Additionally, the present disclosure relates to systems and methods for intelligently detecting anomalies within data sets.