Jurimetrics and artificial intelligence for Indonesian tax court decisions: towards an integrated decision support system

Indonesia’s Tax Court faces a rapidly growing caseload and limited judicial capacity, leading to prolonged dispute resolution and eroding public trust. Manual review processes create inconsistencies that undermine legal certainty, particularly within Indonesia’s civil law system, where jurisprudence is nonbinding and a judge’s personal knowledge may serve as admissible evidence. To address these challenges, we introduce the AI-driven Decision and Integrity for Tax Court Law (ADIL) pipeline, which automates the analysis of 4377 Indonesian Tax Court decisions from 2008 to 2023. ADIL applies advanced natural language processing and legal informatics to extract structured insights from unstructured judicial texts through a trilayered architecture: (i) rule-based pattern matching for high-precision extraction, (ii) transformer-based named-entity recognition for robust entity detection, and (iii) local large-language-model prompting for semantic summarisation and classification. Using exclusively pre-decision metadata, 49 structured metadata fields, including dispute number, tax year, judge identity, and verdict, were used to train a model that resulted in several machine-learning models to predict case outcomes and resolution duration. ADIL achieved 97.5% precision and strong macro-F1 performance in outcome prediction, and an R 2 of 92.9% (RMSE = 63.8%) for duration estimation, equivalent to 7–8 months of predictive accuracy. Notably, incorporating encoded judge identity improved accuracy by 11–16%, revealing systematic variation in judicial behaviour. Feature-importance analysis suggests that ADIL can substantially enhance efficiency, consistency, and transparency in tax adjudication. ADIL offers a data-driven decision support framework towards a more agile and accountable judicial system in Indonesia’s legal and institutional context.