Design-time business process compliance assessment based on multi-granularity semantic information

Business process compliance is an essential part of business process management, which saves organizations from penalties caused by non-compliant processes. However, current researches on business process compliance mainly focus on checking using general constraint rules that have been formalized without in-depth analysis of related regulatory documents and mostly involve extensive human efforts. In this paper, we aim to propose an automatic and interpretable compliance checking approach for design-time business processes. By combining deep learning and a natural language processing approach based on rule templates, we extract semantic information from regulatory documents at different granularities for subsequent compliance checking. In addition, we match appropriate rules to the process model and detect the degree of violation of the business process from three control-flow perspectives. The effectiveness of this method is validated on two real-world datasets.

Automated summary

This paper proposes an automatic and interpretable compliance checking approach for design-time business processes. The approach combines deep learning and natural language processing to extract semantic information from regulatory documents for compliance checking. The effectiveness of this approach is validated on two real-world datasets.