# AI-Enhanced Business Process Automation: A Case Study in the Insurance Domain Using Object-Centric Process Mining

arXiv: https://arxiv.org/abs/2504.17295 · HTML: https://mlsanity.com/paper/2504.17295/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Discovering Object-Centric Petri Nets](https://mlsanity.com/paper/2010.02047/) (2020)
- [OCEL (Object-Centric Event Log) 2.0 Specification](https://mlsanity.com/paper/2403.01975/) (2024)
- [Object Type Clustering using Markov Directly-Follow Multigraph in Object-Centric Process Mining](https://mlsanity.com/paper/2206.11017/) (2022)
- [Transforming Object-Centric Event Logs to Temporal Event Knowledge Graphs (Extended Version)](https://mlsanity.com/paper/2406.07596/) (2024)
- [Advancing Object-Centric Process Mining with Multi-Dimensional Data Operations](https://mlsanity.com/paper/2412.00393/) (2024)
- [Analyzing An After-Sales Service Process Using Object-Centric Process Mining: A Case Study](https://mlsanity.com/paper/2310.10174/) (2023)
- [Towards a Theory on Process Automation Effects](https://mlsanity.com/paper/2506.10992/) (2025)

## Built on by (most cited first, 4 of 4)

- [RSafe: Incentivizing proactive reasoning to build robust and adaptive LLM safeguards](https://mlsanity.com/paper/2506.07736/) (2025)
- [DMN-Guided Prompting: A Framework for Controlling LLM Behavior](https://mlsanity.com/paper/2505.11701/) (2025)
- [Process Constitutions and Process Stewards: Towards the Next Generation of BPM for Agentic Organizations](https://mlsanity.com/paper/2610.05942/) (2026)
- [AI-Decision Checkpoints for AI-Augmented Business Process Management: Framework and Educational Instantiation](https://mlsanity.com/paper/2610.06207/) (2026)

## Abstract (arXiv)

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.