# E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing

arXiv: https://arxiv.org/abs/2505.20733 · HTML: https://mlsanity.com/paper/2505.20733/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 8 of 8)

- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [AutoAgents: A Framework for Automatic Agent Generation](https://mlsanity.com/paper/2309.17288/) (2023)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture](https://mlsanity.com/paper/2309.01105/) (2023)
- [Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges](https://mlsanity.com/paper/2308.05391/) (2023)
- [Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform](https://mlsanity.com/paper/2501.00750/) (2025)
- [ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech Recognition Using LLMs](https://mlsanity.com/paper/2406.18120/) (2024)

## Built on by (most cited first, 5 of 5)

- [A Study on the MCP x A2A Framework for Enhancing Interoperability of LLM-based Autonomous Agents](https://mlsanity.com/paper/2506.01804/) (2025)
- [A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AI](https://mlsanity.com/paper/2509.00398/) (2025)
- [A Self-Healing Framework for Reliable LLM-Based Autonomous Agents](https://mlsanity.com/paper/2605.06737/) (2026)
- [Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges](https://mlsanity.com/paper/2605.02592/) (2026)
- [Novel Architecture of RPA In Oral Cancer Lesion Detection](https://mlsanity.com/paper/2603.10928/) (2026)

## Abstract (arXiv)

This paper presents an intelligent work automation approach in the context of contemporary digital transformation by integrating generative AI and Intelligent Document Processing (IDP) technologies with an Automation Agent to realize End-to-End (E2E) automation of corporate financial expense processing tasks. While traditional Robotic Process Automation (RPA) has proven effective for repetitive, rule-based simple task automation, it faces limitations in handling unstructured data, exception management, and complex decision-making. This study designs and implements a four-stage integrated process comprising automatic recognition of supporting documents such as receipts via OCR/IDP, item classification based on a policy-driven database, intelligent exception handling supported by generative AI (large language models, LLMs), and human-in-the-loop final decision-making with continuous system learning through an Automation Agent. Applied to a major Korean enterprise (Company S), the system demonstrated quantitative benefits including over 80% reduction in processing time for paper receipt expense tasks, decreased error rates, and improved compliance, as well as qualitative benefits such as enhanced accuracy and consistency, increased employee satisfaction, and data-driven decision support. Furthermore, the system embodies a virtuous cycle by learning from human judgments to progressively improve automatic exception handling capabilities. Empirically, this research confirms that the organic integration of generative AI, IDP, and Automation Agents effectively overcomes the limitations of conventional automation and enables E2E automation of complex corporate processes. The study also discusses potential extensions to other domains such as accounting, human resources, and procurement, and proposes future directions for AI-driven hyper-automation development.