Prosecutors across Mexico face growing backlogs due to high caseloads and limited institutional capacity. This paper presents a machine learning (ML) system co-developed with the Zacatecas State Prosecutor's Office to support internal case triage. Focusing on the Módulo de Atención Temprana (MAT) -- the unit responsible for intake and early-stage case resolution -- we train classification models on administrative data from the state's digital case management system (PIE) to predict which open cases are likely to finalize within six months. The model generates weekly ranked lists of 300 cases to assist prosecutors in identifying actionable files. Using historical data from 2014 to 2024, we evaluate model performance under real-time constraints, finding that Random Forest classifiers achieve a mean Precision@300 of 0.74. The system emphasizes interpretability and operational feasibility, and we will test it via a randomized controlled trial. Our results suggest that data-driven prioritization can serve as a low-overhead tool for improving prosecutorial efficiency without disrupting existing workflows.

翻译：墨西哥各地的检察官因案件量高企和机构能力有限而面临日益严重的积压问题。本文介绍了一个与萨卡特卡斯州检察官办公室共同开发的机器学习系统，旨在支持内部案件分流。我们聚焦于早期关注模块——负责案件受理和早期阶段解决的部门——利用该州数字案件管理系统中的行政数据训练分类模型，以预测哪些未结案件可能在六个月内完结。该模型每周生成包含300个案件的排序列表，以协助检察官识别可处理的档案。使用2014年至2024年的历史数据，我们在实时约束下评估模型性能，发现随机森林分类器在Precision@300指标上达到0.74的平均值。该系统强调可解释性和操作可行性，并将通过随机对照试验进行测试。我们的结果表明，数据驱动的优先级排序可作为改善检察效率的低成本工具，且不会干扰现有工作流程。