Enterprise’s internal control for knowledge discovery in a big data environment by an integrated hybrid model

This research aims to (1) identify the critical risk factors that influence the governance of enterprise internal control in a big data environment, (2) depict the intertwined and complicated relationships among risk factors, and (3) yield an attainable target for performance improvement over both the short term and long term. To address these challenging issues, we propose an innovative hybrid decision architecture that combines artificial intelligence-based rule generation techniques and a multiple attribute decision making approach, called herein multiple rule-base decision making. Examining real cases, our study shows that the control environment and information technology (IT) control construction are the top dimension and criterion, respectively. This finding can be taken as a reference for managing and controlling risk factors under a big data environment. In an upcoming improvement/advancement on internal control/information technology (IT) governance, the related factors can also be viewed as essential requirements for enterprises when conducting effective internal control and audit inspection, which can help with more audit success and less lawsuit problems.