Abstract: Classifying documents is provided. A document is received for classification. The classification of the document is performed at an individual page level and an entire document level based on classification confidence scores generated by the machine learning document classification model at the individual page level and the entire document level to increase classification accuracy.

Abstract: A method is provided. The method may include, in response to electronically receiving a document, automatically classifying the document and different parts of the document, by electronically identifying a document type associated with the document and electronically tagging data associated with the different parts of the document based on classification rules. The method may further include automatically extracting the tagged data associated with the automatically classified document based on data extraction rules. The method may further include detecting first feedback associated with the classification rules and second feedback associated with the data extraction rules. The method may further include automatically generating and updating validation rules based on the identified document type, the detected first feedback, and the detected second feedback to validate the automatically classified document and the automatically tagged and extracted data.

Abstract: A method is provided. The method may include, in response to electronically receiving a document, automatically classifying the document and different parts of the document, by electronically identifying a document type associated with the document and electronically tagging data associated with the different parts of the document based on classification rules. The method may further include automatically extracting the tagged data associated with the automatically classified document based on data extraction rules. The method may further include detecting first feedback associated with the classification rules and second feedback associated with the data extraction rules. The method may further include automatically generating and updating validation rules based on the identified document type, the detected first feedback, and the detected second feedback to validate the automatically classified document and the automatically tagged and extracted data.

Abstract: A data harvester enhances compliance audits by characterizing data sources, sampling data in one or more of the data sources to determine likelihood of success of the data harvest, estimating time for the data harvest, making recommendations from the samples based on machine learning relating to previous runs, then sampling additional data while estimated expected completion time. The harvested data may then be analyzed and compared to compliance requirements, and a compliance report may be generated.

Abstract: A data harvester enhances compliance audits by characterizing data sources, sampling data in one or more of the data sources to determine likelihood of success of the data harvest, estimating time for the data harvest, making recommendations from the samples based on machine learning relating to previous runs, then sampling additional data while estimated expected completion time. The harvested data may then be analyzed and compared to compliance requirements, and a compliance report may be generated.