Abstract: Systems and methods are provided for accessing master data comprising a plurality of representative data records, where each representative data record represents a cluster of similar data records, and each similar data record has a confidence score indicating a confidence level that the similar data record corresponds to the cluster, and comparing a new data record to each representative data record of the plurality of representative data records using a machine learning model to generate a distance score. The systems and methods further provide for analyzing the cluster of similar data records corresponding to each representative data record in a selected set of representative data records to generate candidate values for the requested data field of the new data record, and generating a candidate score for each of the candidate values using the distance score and the confidence score to use in providing a recommended candidate value.

Abstract: Systems and methods are provided for accessing master data comprising a plurality of representative data records, where each representative data record represents a cluster of similar data records, and each similar data record has a confidence score indicating a confidence level that the similar data record corresponds to the cluster, and comparing a new data record to each representative data record of the plurality of representative data records using a machine learning model to generate a distance score. The systems and methods further provide for analyzing the cluster of similar data records corresponding to each representative data record in a selected set of representative data records to generate candidate values for the requested data field of the new data record, and generating a candidate score for each of the candidate values using the distance score and the confidence score to use in providing a recommended candidate value.