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Organizations face the challenge of ensuring compliance with an increasingamount of requirements from various regulatory documents. Which requirementsare relevant depends on aspects such as the geographic location of theorganization, its domain, size, and business processes. Considering thesecontextual factors, as a first step, relevant documents (e.g., laws,regulations, directives, policies) are identified, followed by a more detailedanalysis of which parts of the identified documents are relevant for which stepof a given business process. Nowadays the identification of regulatoryrequirements relevant to business processes is mostly done manually by domainand legal experts, posing a tremendous effort on them, especially for a largenumber of regulatory documents which might frequently change. Hence, this workexamines how legal and domain experts can be assisted in the assessment ofrelevant requirements. For this, we compare an embedding-based NLP rankingmethod, a generative AI method using GPT-4, and a crowdsourced method with thepurely manual method of creating relevancy labels by experts. The proposedmethods are evaluated based on two case studies: an Australian insurance casecreated with domain experts and a global banking use case, adapted from SAPSignavio's workflow example of an international guideline. A gold standard iscreated for both BPMN2.0 processes and matched to real-world textualrequirements from multiple regulatory documents. The evaluation and discussionprovide insights into strengths and weaknesses of each method regardingapplicability, automation, transparency, and reproducibility and provideguidelines on which method combinations will maximize benefits for givencharacteristics such as process usage, impact, and dynamics of an applicationscenario.

Identification of Regulatory Requirements Relevant to Business  Processes: A Comparative Study on Generative AI, Embedding-based Ranking,  Crowd and Expert-driven Methods