Abstract: An embodiment for managing machine learning models to generate and utilize perforations within machine learning models to improve their ability to consider and learn from exception decisions. The embodiment may detect an exception decision in a base model. The embodiment may automatically determine data considered in making the exception decision and identify and store in a database known features from the gathered data. The embodiment may automatically identify and store in the database remaining additional features considered, and generate and store perforations corresponding to the remaining additional features considered. The embodiment may, in response to detecting subsequent decisions involving shared additional features contained in the generated perforations, automatically validate feature boundaries within the generated perforations from a set of data sources.

Abstract: A method, system, and computer program product for source record management for master data are provided in the illustrative embodiments. A set of data records is received from a set of data sources. A first subset of data records received from a first data source is pre-processed. A match engine is requested to match a data record from the first subset using at least one record in the master data repository, the requesting resulting in a set of matched data records. The set of matched data records, which includes a first data record in the first subset and a second data record in a second subset, is post-processed. The second subset is received from a second data source. The first data record is assigned to a group of records, together representing the entity as a master data record.