Process Mining for legal Courts: Visualising, analysing and comparing Italian divorce proceedings

Process Mining (PM) is a family of data-driven techniques that use data to study the underlying processes generating the data, i.e., the data-generating process. Despite being initially tailored for the engineering and industrial domain, it is becoming popular also in more human-centric domains like the legal and healthcare ones. We present a PM methodology using the fuzzy miner technique aimed at analysing and optimising the complex processes underlying decision making by legal Courts. We consider specifically the domain of civil proceedings, with a focus on divorces. In PM terms, we see a legal proceeding as a process instance, and the different internal phases in which a legal proceeding transits as activities. The studied process is, therefore, the internal process followed by a Court, possibly varying over the years, to handle specific types of proceedings. By leveraging PM techniques, this article compares consensual divorce proceedings within a Court across time, and across Courts. As a case study we take two Courts in Northern Italy. Our PM analysis identifies key performance indicators and uncovers hidden process efficiencies and inefficiencies. The findings highlight the ability of PM to reveal critical process patterns, enabling organisations to make data-driven decisions and implement targeted process improvements.