Machine-learned redlining classification

An artificial intelligence system comprising:

• one or more processors;
• and
• a non-transitory computer readable storage medium storing executable instructions that, when executed by the one or more processors, cause the one or more processors to perform steps comprising:
• accessing, by an artificial intelligence system, a set of training documents corresponding to document templates of respective document types, each training document including one or more sets of edits made by one or more entities;
• training, by the artificial intelligence system, a machine-learned model using the set of training documents, the machine-learned model configured to, when applied to a document from a counterparty entity, classify the document as having a type of the respective document types;
• applying, by the artificial intelligence system, the machine-learned model to a target document to classify a document type of the target document;
• comparing, by the artificial intelligence system, the target document against a first template of the classified document type to identify an edited portion of the target document and unedited portion of the target document, wherein the edited portion includes a first set of edits and a second set of edits, the first set of edits similar to edits made to one or more of the set of training documents and the second set of edits distinct from edits made to the set of training documents;
• modifying, by the artificial intelligence system, the unedited portion of the target document using a predetermined edit associated with a second template;
• and
• displaying, by the artificial intelligence system, the target document to a viewing entity such that the modified portion is visually distinct from the edited portion.

A non-transitory computer readable storage medium storing executable instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising:

• accessing, by an artificial intelligence system, a set of training documents corresponding to document templates of respective document types, each training document including one or more sets of edits made by one or more entities;
• training, by the artificial intelligence system, a machine-learned model using the set of training documents, the machine-learned model configured to, when applied to a document from a counterparty entity, classify the document as having a type of the respective document types;
• applying, by the artificial intelligence system, the machine-learned model to a target document to classify a document type of the target document;
• comparing, by the artificial intelligence system, the target document against a first template of the classified document type to identify an edited portion of the target document and unedited portion of the target document, wherein the edited portion includes a first set of edits and a second set of edits, the first set of edits similar to edits made to one or more of the set of training documents and the second set of edits distinct from edits made to the set of training documents;
• modifying, by the artificial intelligence system, the unedited portion of the target document using a predetermined edit associated with a second template;
• and
• displaying, by the artificial intelligence system, the target document to a viewing entity such that the modified portion is visually distinct from the edited portion.