Investigation of Lawsuit Process Duration: A Machine Learning and Process Mining Approach

Abstract

Delays in legal proceedings can significantly harm both corporate finances and individual lives. While many jurisdictions rely on subjective criteria such as reasonable process duration to manage case processing times, machine learning offers an alternative approach by estimating lawsuit durations and assessing the impact of multiple variables on these timelines. This study explores the use of machine learning to estimate lawsuit duration, considering multiple variables, including control-flow characteristics. We observed that the sequences of procedural movements exhibit complex patterns, making them suitable for analysis using clustering techniques employed in process mining. We framed the problem as a supervised learning regression task, incorporating clustering results as additional features. Employing linear regression, support vector regression, and gradient boosting methods, we analyzed over 60,000 cases from Brazilian labor courts. The gradient boosting model achieved an R2-score of 0.87, and feature importance analysis revealed that clustering techniques features emerged among the most significant in time estimation.