  Organizations face the challenge of ensuring compliance with an increasing
amount of requirements from various regulatory documents. Which requirements
are relevant depends on aspects such as the geographic location of the
organization, its domain, size, and business processes. Considering these
contextual factors, as a first step, relevant documents (e.g., laws,
regulations, directives, policies) are identified, followed by a more detailed
analysis of which parts of the identified documents are relevant for which step
of a given business process. Nowadays the identification of regulatory
requirements relevant to business processes is mostly done manually by domain
and legal experts, posing a tremendous effort on them, especially for a large
number of regulatory documents which might frequently change. Hence, this work
examines how legal and domain experts can be assisted in the assessment of
relevant requirements. For this, we compare an embedding-based NLP ranking
method, a generative AI method using GPT-4, and a crowdsourced method with the
purely manual method of creating relevancy labels by experts. The proposed
methods are evaluated based on two case studies: an Australian insurance case
created with domain experts and a global banking use case, adapted from SAP
Signavio's workflow example of an international guideline. A gold standard is
created for both BPMN2.0 processes and matched to real-world textual
requirements from multiple regulatory documents. The evaluation and discussion
provide insights into strengths and weaknesses of each method regarding
applicability, automation, transparency, and reproducibility and provide
guidelines on which method combinations will maximize benefits for given
characteristics such as process usage, impact, and dynamics of an application
scenario.
