Business Knowledge and Neural Learning: organisation-specific transformer via semantic pre-training

AI approaches to business knowledge management have often neglected the role of documents, which are the backbone of expertise, norms, and optimal practices that every organisation implicitly encodes in its large-scale document collections. Banks make no exception and have to deal with operational documents on business process engineering, as well as norms on legal compliance aspects. They are thus particularly interested in the mining of the huge body of knowledge implicitly stored in their text archives, i.e. in their document assets. Extracting semantic metadata from raw bank documents is therefore central for supporting effective governance, business engineering as well as legal monitoring processes in an accurate and profitable manner. In this paper, a weakly-supervised neural methodology for creating semantic metadata from bank documents and its application to different banking organisations is presented. Based on a neural pre-training methodology driven by knowledge models of individual banks, it is shown to improve with respect to inductive approaches previously presented, that are domain specific, but organisation independent. The application to business process design in different Italian banks has been here tested and the observed impact through measurements confirms its wide applicability at the level of banks, as well as to other business organisations.