Research on an AI-based Smart Management Information System for Enterprise Judicial Cases: A Case Study of Power Grid Enterprises
- DOI
- 10.2991/978-94-6463-638-3_33How to use a DOI?
- Keywords
- Judicial Cases; Legal Management; Information System; Legal Artificial Intelligence
- Abstract
Enterprise legal management is a crucial part of overall corporate management, particularly in handling a vast amount of judicial document data related to legal cases. Currently, due to limitations in data analysis and processing capabilities, enterprises have not fully harnessed the value of their case data, and the decision-support and risk prevention roles of intelligent case management are not fully realized. This paper presents a method for constructing an AI-based smart management information system for enterprise judicial cases, using power grid enterprises as a case study. It explores how to utilize AI technology at the intersection of law and electricity to process corporate case texts and extract valuable data, aiming to empower enterprise case management through big data and AI technology.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jiayun Shi AU - Lei Xu AU - Chengyan Huang AU - Xiaoyun Zha AU - Jin Liu PY - 2024 DA - 2024/12/30 TI - Research on an AI-based Smart Management Information System for Enterprise Judicial Cases: A Case Study of Power Grid Enterprises BT - Proceedings of the 5th International Conference on Economic Management and Big Data Application (ICEMBDA 2024) PB - Atlantis Press SP - 322 EP - 333 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-638-3_33 DO - 10.2991/978-94-6463-638-3_33 ID - Shi2024 ER -