Abstract: Methods, systems, and computer-readable storage media for receiving a first bank statement at a hybrid pipeline including a set of lookup tables and a deep learning (DL) model that can each be used to determine customer IDs from bank statements, providing a first key based on data associated with the first bank statement, and determining that the first key is included in a first lookup table of the set of lookup tables, and in response: identifying a first set of customer IDs from the first lookup table, the first set of customer IDs including one or more customer IDs, and outputting the first set of customer IDs to computer-executable software that matches the first bank statement to one or more electronic documents at least partially based on the first set of customer IDs.

Abstract: Methods, systems, and computer-readable storage media for receiving input data including a set of entities of a first type and a set of entities of a second type, providing a set of features based on entities of the first type, the set of features including features expected to be included in entities of the second type, filtering entities of the second type based on the set of features to provide a sub-set of entities of the second type, and generating an output by processing the set of entities of the first type and the sub-set of entities of the second type through a ML model, the output comprising a set of matching pairs, each matching pair in the set of matching pairs comprising an entity of the set of entities of the first type and at least one entity of the sub-set of entities of the second type.

Abstract: Methods, systems, and computer-readable storage media for a machine learning (ML) model and framework for training of the ML model to enable the ML model to correctly match entities even in instances where new entities are added after the ML model has been trained. More particularly, implementations of the present disclosure are directed to a ML model provided as a neural network that is trained to provide a scalar confidence score that indicates whether two entities in a pair of entities are considered a match, even if an entity in the set of entities was not accounted for in training of the ML model.

Abstract: Methods, systems, and computer-readable storage media for tuning behavior of a machine learning (ML) model by providing an alternative loss function used during training of a ML model, the alternative loss function enhancing reliability of the ML model, calibrating the confidence of the ML model after training, and reducing risk in downstream tasks by providing a mapping between the confidence of the ML model to the expected accuracy of the ML model.

Abstract: Methods, systems, and computer-readable storage media for a machine learning (ML) model and framework for training of the ML model to enable the ML model to correctly match entities even in instances where new entities are added after the ML model has been trained. More particularly, implementations of the present disclosure are directed to a ML model provided as a neural network that is trained to provide a scalar confidence score that indicates whether two entities in a pair of entities are considered a match, even if an entity in the set of entities was not accounted for in training of the ML model.

Abstract: Methods, systems, and computer-readable storage media for tuning behavior of a machine learning (ML) model by providing an alternative loss function used during training of a ML model, the alternative loss function enhancing reliability of the ML model, calibrating the confidence of the ML model after training, and reducing risk in downstream tasks by providing a mapping between the confidence of the ML model to the expected accuracy of the ML model.

Abstract: Methods, systems, and computer-readable storage media for receiving input data including a set of entities of a first type and a set of entities of a second type, providing a set of features based on entities of the first type, the set of features including features expected to be included in entities of the second type, filtering entities of the second type based on the set of features to provide a sub-set of entities of the second type, and generating an output by processing the set of entities of the first type and the sub-set of entities of the second type through a ML model, the output comprising a set of matching pairs, each matching pair in the set of matching pairs comprising an entity of the set of entities of the first type and at least one entity of the sub-set of entities of the second type.

Abstract: Methods, systems, and computer-readable storage media for receiving a first bank statement at a hybrid pipeline including a set of lookup tables and a deep learning (DL) model that can each be used to determine customer IDs from bank statements, providing a first key based on data associated with the first bank statement, and determining that the first key is included in a first lookup table of the set of lookup tables, and in response: identifying a first set of customer IDs from the first lookup table, the first set of customer IDs including one or more customer IDs, and outputting the first set of customer IDs to computer-executable software that matches the first bank statement to one or more electronic documents at least partially based on the first set of customer IDs.