Systems and methods for data entity matching between disparate datasets

A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:

• receive from a first data source, a reference data object comprising a plurality of data elements;
• generate a text representation for the reference data object according to a predefined sequence, wherein the text representation comprises a string value comprising at least two data elements, of the plurality of data elements, positioned in accordance with the predefined sequence;
• generate a reference numeric vector for a reference data object from a first data source based on a text representation associated with the reference data object;
• generate a plurality of match candidate numeric vectors for a plurality of match candidate data objects from a second data source based on a plurality of text representations associated with the plurality of match candidate data objects;
• identify a subset of match candidate data objects from the plurality of match candidate data objects based on a comparison between the reference numeric vector and the plurality of match candidate numeric vectors;
• generate a plurality of data features based on a comparison between (a) a plurality of reference object attributes corresponding to the reference data object and (b) a plurality of matching object attributes corresponding to the subset of match candidate data objects;
• provide the plurality of data features to an entity matching machine learning model to generate a plurality of match scores, wherein a match score of the plurality of match scores comprises a probabilistic value of a match likelihood between one of the subset of match candidate data objects and the reference data object;
• and
• identify a match between the reference data object and a match candidate data object of the subset of match candidate data objects based on the plurality of match scores and a threshold.

One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to:

• receive from a first data source, a reference data object comprising a plurality of data elements;
• generate a text representation for the reference data object according to a predefined sequence, wherein the text representation comprises a string value comprising at least two data elements, of the plurality of data elements, positioned in accordance with the predefined sequence;
• generate a reference numeric vector for a reference data object from a first data source based on a text representation associated with the reference data object;
• generate a plurality of match candidate numeric vectors for a plurality of match candidate data objects from a second data source based on a plurality of text representations associated with the plurality of match candidate data objects;
• identify a subset of match candidate data objects from the plurality of match candidate data objects based on a comparison between the reference numeric vector and the plurality of match candidate numeric vectors;
• generate a plurality of data features based on a comparison between (a) a plurality of reference object attributes corresponding to the reference data object and (b) a plurality of matching object attributes corresponding to the subset of match candidate data objects;
• provide the plurality of data features to an entity matching machine learning model to generate a plurality of match scores, wherein a match score of the plurality of match scores comprises a probabilistic value of a match likelihood between one of the subset of match candidate data objects and the reference data object;
• and
• identify a match between the reference data object and a match candidate data object of the subset of match candidate data objects based on the plurality of data features.