Abstract
This study investigates the integration of Decision Model and Notation (DMN) with generative artificial intelligence (AI) to support the dynamic and explainable automation of decision-making processes in the domain of tax-related accounting. Addressing a recognized gap in the literature, we propose and empirically validate a hybrid human-AI framework for decision rule generation, implemented within LUCA - an enterprise-grade system deployed across Swiss accounting offices. The system leverages BPMN-driven workflows, AI-supported document understanding, and DMN-based rule management to formalize tacit expert knowledge. The methodology is grounded in a descriptive case study approach, utilizing participant observation and iterative validation. Key findings indicate a substantial improvement in automation efficiency, enhanced auditability, and effective rule reuse across distributed organizational units. We demonstrate that combining generative AI with DMN enables scalable, high-fidelity decision modeling while preserving transparency and human oversight. The study contributes a replicable model of knowledge acquisition and governance for data- and regulation-intensive environments.
Paper Type
Short Paper
DOI
10.62036/ISD.2025.98
Dynamic generation of Decision Model and Notation rules for tax regulations – case study from Swiss accounting offices
This study investigates the integration of Decision Model and Notation (DMN) with generative artificial intelligence (AI) to support the dynamic and explainable automation of decision-making processes in the domain of tax-related accounting. Addressing a recognized gap in the literature, we propose and empirically validate a hybrid human-AI framework for decision rule generation, implemented within LUCA - an enterprise-grade system deployed across Swiss accounting offices. The system leverages BPMN-driven workflows, AI-supported document understanding, and DMN-based rule management to formalize tacit expert knowledge. The methodology is grounded in a descriptive case study approach, utilizing participant observation and iterative validation. Key findings indicate a substantial improvement in automation efficiency, enhanced auditability, and effective rule reuse across distributed organizational units. We demonstrate that combining generative AI with DMN enables scalable, high-fidelity decision modeling while preserving transparency and human oversight. The study contributes a replicable model of knowledge acquisition and governance for data- and regulation-intensive environments.
Recommended Citation
Makowski, M. & Trąbka, J. (2025). Dynamic generation of Decision Model and Notation rules for tax regulations – case study from Swiss accounting officesIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.98