FinRobot: Generative Business Process AI Agents for Enterprise Resource Planning in Finance

• URL: http://arxiv.org/abs/2506.01423v1
• Date: Mon, 02 Jun 2025 08:22:28 GMT
• Title: FinRobot: Generative Business Process AI Agents for Enterprise Resource Planning in Finance
• Authors: Hongyang Yang, Likun Lin, Yang She, Xinyu Liao, Jiaoyang Wang, Runjia Zhang, Yuquan Mo, Christina Dan Wang,
• Abstract summary: We present the first AI-native framework for ERP systems, introducing a novel architecture of Generative Business Process AI Agents.<n>The proposed system integrates generative AI with business process modeling and multi-agent orchestration, enabling end-to-end automation.<n>We show that GBPAs achieve up to 40% reduction in processing time, 94% drop in error rate, and improved regulatory compliance.
• Score: 6.494553545846438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Enterprise Resource Planning (ERP) systems serve as the digital backbone of modern financial institutions, yet they continue to rely on static, rule-based workflows that limit adaptability, scalability, and intelligence. As business operations grow more complex and data-rich, conventional ERP platforms struggle to integrate structured and unstructured data in real time and to accommodate dynamic, cross-functional workflows. In this paper, we present the first AI-native, agent-based framework for ERP systems, introducing a novel architecture of Generative Business Process AI Agents (GBPAs) that bring autonomy, reasoning, and dynamic optimization to enterprise workflows. The proposed system integrates generative AI with business process modeling and multi-agent orchestration, enabling end-to-end automation of complex tasks such as budget planning, financial reporting, and wire transfer processing. Unlike traditional workflow engines, GBPAs interpret user intent, synthesize workflows in real time, and coordinate specialized sub-agents for modular task execution. We validate the framework through case studies in bank wire transfers and employee reimbursements, two representative financial workflows with distinct complexity and data modalities. Results show that GBPAs achieve up to 40% reduction in processing time, 94% drop in error rate, and improved regulatory compliance by enabling parallelism, risk control insertion, and semantic reasoning. These findings highlight the potential of GBPAs to bridge the gap between generative AI capabilities and enterprise-grade automation, laying the groundwork for the next generation of intelligent ERP systems.

Related papers

• A Survey on Agent Workflow -- Status and Future [2.817843718857682]
  This survey provides a comprehensive review of agent workflow systems.<n>We classify existing systems along two key dimensions: functional capabilities and architectural features.<n>We highlight common patterns, potential technical challenges, and emerging trends.
  arXiv  Detail & Related papers  (2025-08-02T04:15:30Z)
• Agentic Web: Weaving the Next Web with AI Agents [109.13815627467514]
  The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web.<n>In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users.<n>We present a structured framework for understanding and building the Agentic Web.
  arXiv  Detail & Related papers  (2025-07-28T17:58:12Z)
• Efficient and Scalable Agentic AI with Heterogeneous Systems [1.8921715645847679]
  AI agents are emerging as a dominant workload in a wide range of applications, promising to be the vehicle that delivers the promised benefits of AI to enterprises and consumers.<n>To scale AI agent usage, we need efficient and scalable deployment and agent-serving infrastructure.<n>We present a system design for dynamic orchestration of AI agent workloads on heterogeneous compute infrastructure.
  arXiv  Detail & Related papers  (2025-07-25T19:02:42Z)
• Toward Data Systems That Are Business Semantic Centric and AI Agents Assisted [0.0]
  Business Semantics Centric, AI Agents Assisted Data System (BSDS)<n>BSDS redefines data systems as dynamic enablers of business success.<n>System includes curated data linked to business entities, knowledge base for context-aware AI agents, and efficient data pipelines.
  arXiv  Detail & Related papers  (2025-06-05T19:06:06Z)
• E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing [1.5728609542259502]
  This paper presents an intelligent work automation approach in the context of contemporary digital transformation.<n>It integrates generative AI and Intelligent Document Processing technologies with an Automation Agent to realize End-to-End (E2E) automation of corporate financial expense processing tasks.
  arXiv  Detail & Related papers  (2025-05-27T05:21:08Z)
• LLM-Powered AI Agent Systems and Their Applications in Industry [3.103098467546532]
  Large Language Models (LLMs) have reshaped agent systems.<n>Unlike traditional rule-based agents with limited task scope, LLM-powered agents offer greater flexibility, cross-domain reasoning, and natural language interaction.
  arXiv  Detail & Related papers  (2025-05-22T01:52:15Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [59.52058740470727]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework [49.633199780510864]
  This work proposes a multi-agent autonomous mechatronics design framework, integrating expertise across mechanical design, optimization, electronics, and software engineering.<n> operating primarily through a language-driven workflow, the framework incorporates structured human feedback to ensure robust performance under real-world constraints.<n>A fully functional autonomous vessel was developed with optimized propulsion, cost-effective electronics, and advanced control.
  arXiv  Detail & Related papers  (2025-04-20T16:57:45Z)
• Autonomous Deep Agent [0.7489814067742621]
  Deep Agent is an advanced autonomous AI system designed to manage complex multi-phase tasks.<n>The system's foundation is built on our Hierarchical Task DAG framework.<n>Deep Agent establishes a novel paradigm in self-governing AI systems.
  arXiv  Detail & Related papers  (2025-02-10T21:46:54Z)
• Towards Resource-Efficient Compound AI Systems [4.709762596591902]
  Compound AI Systems integrate multiple interacting components like models, retrievers, and external tools.<n>Current implementations suffer from inefficient resource utilization due to tight coupling between application logic and execution details.<n>We propose a declarative workflow programming model and an adaptive runtime system for dynamic scheduling and resource-aware decision-making.
  arXiv  Detail & Related papers  (2025-01-28T02:15:34Z)
• Flow: Modularized Agentic Workflow Automation [53.073598156915615]
  Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution.<n>However, the effective adjustment of agentic during execution has not been well studied.<n>In this paper, we define an activity-on-vertex (AOV) graph, which allows continuous workflow refinement by agents.<n>Our proposed multi-agent framework achieves efficient concurrent execution of subtasks, effective goal achievement, and enhanced error tolerance.
  arXiv  Detail & Related papers  (2025-01-14T04:35:37Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose AOP, a novel framework for agent-oriented planning in multi-agent systems.<n>In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy.<n> Extensive experiments demonstrate the advancement of AOP in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• ComfyBench: Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI Systems [80.69865295743149]
  This work attempts to study using LLM-based agents to design collaborative AI systems autonomously.<n>Based on ComfyBench, we develop ComfyAgent, a framework that empowers agents to autonomously design collaborative AI systems by generating.<n>While ComfyAgent achieves a comparable resolve rate to o1-preview and significantly surpasses other agents on ComfyBench, ComfyAgent has resolved only 15% of creative tasks.
  arXiv  Detail & Related papers  (2024-09-02T17:44:10Z)
• DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning [56.887047551101574]
  We present DS-Agent, a novel framework that harnesses large language models (LLMs) agent and case-based reasoning (CBR) In the development stage, DS-Agent follows the CBR framework to structure an automatic iteration pipeline, which can flexibly capitalize on the expert knowledge from Kaggle. In the deployment stage, DS-Agent implements a low-resource deployment stage with a simplified CBR paradigm, significantly reducing the demand on foundational capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-02-27T12:26:07Z)
• The Foundations of Computational Management: A Systematic Approach to Task Automation for the Integration of Artificial Intelligence into Existing Workflows [55.2480439325792]
  This article introduces Computational Management, a systematic approach to task automation. The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
  arXiv  Detail & Related papers  (2024-02-07T01:45:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.