用AI自动化保险理赔识别，提升效率并发现新流程问题。
AI-Enhanced Business Process Automation: A Case Study in the Insurance Domain Using Object-Centric Process Mining
- 用大模型自动识别理赔单据部件，替代人工
- 自动化使处理能力显著提升，但流程出现新动态
- 验证了面向真实场景的流程挖掘方法价值
人工智能，尤其是大语言模型（LLMs）的进展，提升了组织通过自动化知识密集型任务来重构业务流程的能力。这一自动化推动数字化转型，通常以渐进方式提升流程效率与效果。为全面评估此类自动化的影响，需要数据驱动的方法来分析传统与AI增强流程变体在转型过程中的共存情况。对象中心流程挖掘（OCPM）已成为实现此类分析的有力工具，但真实世界案例仍显不足。本文展示了一个来自保险领域的案例研究，其中部署生产级大模型以自动化识别理赔单据部件，该任务曾是人工操作的瓶颈，制约了可扩展性。为评估此转型，我们应用OCPM分析AI自动化对流程可扩展性的影响。研究发现，尽管大模型显著提升了运营能力，但也引入了新的流程动态，需进一步优化。本研究还展示了OCPM在真实场景中的实际应用，突出了其优势与局限性。
原文摘要 · Abstract (English)
Recent advancements in Artificial Intelligence (AI), particularly Large Language Models (LLMs), have enhanced organizations' ability to reengineer business processes by automating knowledge-intensive tasks. This automation drives digital transformation, often through gradual transitions that improve process efficiency and effectiveness. To fully assess the impact of such automation, a data-driven analysis approach is needed - one that examines how traditional and AI-enhanced process variants coexist during this transition. Object-Centric Process Mining (OCPM) has emerged as a valuable method that enables such analysis, yet real-world case studies are still needed to demonstrate its applicability. This paper presents a case study from the insurance sector, where an LLM was deployed in production to automate the identification of claim parts, a task previously performed manually and identified as a bottleneck for scalability. To evaluate this transformation, we apply OCPM to assess the impact of AI-driven automation on process scalability. Our findings indicate that while LLMs significantly enhance operational capacity, they also introduce new process dynamics that require further refinement. This study also demonstrates the practical application of OCPM in a real-world setting, highlighting its advantages and limitations.
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