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

2025-06-02

AI Summary

Traditional financial ERP systems rely on static rule-based logic, rendering them inflexible for complex business processes and incapable of effectively integrating heterogeneous, multi-source data—resulting in rigid workflows, poor real-time responsiveness, and limited intelligence. To address these limitations, this paper proposes the first AI-native Generative Business Process Agent (GBPA) framework specifically designed for financial ERP systems. The framework uniquely integrates large language models (LLMs), BPMN-based process modeling, and multi-agent coordination to enable semantic intent understanding, real-time workflow generation, and risk-aware parallel execution. It supports end-to-end dynamic automation across key financial functions—including budget planning, financial reporting, and wire transfer processing—and performs joint reasoning over structured and unstructured data. Empirical evaluation in wire transfer and expense reimbursement scenarios demonstrates a 40% reduction in processing time and a 94% decrease in error rates, significantly improving regulatory compliance and cross-functional process adaptability.

Technology Category

• Natural Language Processing: Generation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Enhancing adaptability and intelligence in static ERP systems
• Integrating structured and unstructured data in real-time workflows
• Automating complex financial tasks with generative AI agents

Innovation

• AI-native agent-based ERP framework
• Generative AI with process modeling
• Dynamic multi-agent orchestration