Model-driven Analysis and Execution of Insurance Decision Models


With the Decision Model and Notation (DMN) an OMG-maintained industry-standard for modeling and executing business decisions is provided. DMN models enable the specification of complex hierarchical decisions by graphical means as well as the programmatic and automated execution of these decisions. As part of an industrial collaboration, this project aims at investigating the possibilities of using DMN in the insurance sector. The project partner will provide real data that can be used to test the implemented model-based approach.



The task of this project is to first formalize the insurance domain knowledge (i.e., the rule base for calculating insurance rates) in form of DMN decision models and then to process these models programmatically from a Java/Python application in order to realize a web-application that retrieves input values and processes them using the DMN models in order to calculate and return the insurance rate. As an additional task of this project, a given set of DMN models shall be analysed for validity and complexity. This analysis shall yield test case generation that can be used to further and deeply analyse the decisions, thereby identifying statistical measures (min, max, avg, standard deviation) as well as big data measures like kneepoint analysis, regression tests etc.


Further Reading (Excerpt)

  • Camunda DMN Simulator:
  • Hasić, F., & Vanthienen, J. (2020). From decision knowledge to e-government expert systems: the case of income taxation for foreign artists in Belgium. Knowledge and Information Systems, 62(5), 2011-2028.
  • Etinger, D., Simić, S. D., & Buljubašić, L. (2019). Automated decision-making with DMN: from decision trees to decision tables. In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 1309-1313). IEEE.