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FinRobot: Generative Business Process AI Agents for Enterprise Resource Planning in Finance
Hongyang Yang, Likun Lin, Yang She, Xinyu Liao, Jiaoyang Wang, Runjia Zhang, Yuquan Mo, Christina Dan Wang
TL;DR
Traditional ERP systems rely on static workflows that limit adaptation to complex, data-rich financial operations. The paper proposes a layered GBPA framework that combines LLM reasoning, business modeling, and agent orchestration; it reports up to 40% lower processing time and a 94% drop in error rates while meeting regulatory and operational requirements.
Problem
Traditional ERP systems rely on static, rules-based workflows that limit adaptation to complex data and real-time decision-making demands.
Source: §1 · p. 1Method
GBPAs combine generative language models, business semantics, and modular orchestration, with Chain-of-Actions coordinating sub-agents in response to business intent.
Source: §1 · p. 1, §3 · p. 2, §3.5 · p. 4Results
Processing time falls by up to 40% and error rates by 94% with GBPAs, which also meet regulatory and operational requirements.
Source: §1 · p. 1Takeaways & Limitations
The layered GBPA setup demonstrates flexibility in transforming legacy systems into intelligent, adaptive infrastructures for end-to-end process automation.
Source: §4.1 · p. 5
Abstract
from arXiv · showhide
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.
1 Introduction
Traditional ERP workflows rely on static rules that constrain adaptation to complex, heterogeneous financial operations, while LLM deployment must remain auditable and compliant. The paper introduces GBPAs for dynamic workflow planning and reports faster processing, fewer errors, and regulatory and operational compliance.
1 Introduction
- Traditional ERP systems rely on static, rules-based workflows that limit adaptation to complex data and real-time decision-making demands.In finance, these constraints matter for tasks requiring high accuracy, speed, and compliance.Source: §1 · p. 1
- LLM-based ERP agents must support traceable, auditable workflows and domain-specific tasks while meeting regulatory constraints.Existing process-generation approaches and general-purpose agents lack the runtime orchestration or semantic guarantees needed for ERP-grade execution.Source: §1 · p. 1
- Processing time falls by up to 40% and error rates by 94% with GBPAs, which also meet regulatory and operational requirements.The framework was evaluated on inter-bank wire transfers and employee reimbursements.Source: §1 · p. 1
- GBPAs combine LLM reasoning, business modeling, and modular orchestration to interpret intent, decompose goals, and coordinate specialized agents in real time.The framework adapts Chain-of-Actions for task decomposition, context-aware routing, and multi-agent coordination across financial workflows.Source: §1 · pp. 1–2
2 Related Work
Prior ERP automation uses deterministic rules that can struggle with exceptions and changing requirements. AI-in-process and agent approaches add capabilities, but the cited work leaves gaps in dynamic orchestration and enterprise-specific control that GBPA addresses.
2.1 Workflow Automation in ERP
- Rules-based ERP and RPA work well in stable settings but can struggle with exceptions, cross-module workflows, and evolving requirements.The related-work discussion characterizes static automation as increasingly inadequate for dynamic, context-aware execution.Source: §2.1 · p. 2
2.2 AI in ERP
- Large Process Models and ProcessGPT generate process variants or fragments, but primarily focus on process-level generation rather than dynamic orchestration.The section also describes AI integration in ERP for decision support and coordination, and reports cited industry estimates for productivity and development speed.Source: §2.2 · p. 2
2.3 AI Agents in ERP Automation
- General-purpose agents and prior enterprise-agent efforts lack some combination of business-logic constraints, modular pipelines, contextual synthesis, or adaptive task routing.Table 1 compares AI-augmented and AI-native ERP designs across process generation, semantic control, and agent-based execution.Source: §2.3 · p. 2, Table 1 · p. 2
3 Methodology
The methodology presents a layered GBPA architecture centered on Chain-of-Actions, which composes and coordinates agents in response to business intent. Its layers structure business data and intent for model reasoning, plan execution, and deployment into operational systems.
3 Methodology
- The framework centers on Chain-of-Actions, a planning and execution controller that dynamically composes and coordinates sub-agents in response to business intent.The architecture is layered and combines modular agents, structured knowledge, and generative models.Source: §3 · p. 2
3.1 Architectural Layers
- The architecture combines heterogeneous business data, event-centric 5W3H1R representations, business intent modeling, LLM reasoning, CoA execution, and deployment infrastructure.Agents and CoA instances can run as stateless microservices in containerized environments, with orchestration tools and APIs supporting enterprise integration.Source: §3.1 · pp. 2–3
3.2 Data Modeling Layer: Event-Centric Representation via 5W3H1R
- 5W3H1R reformats fragmented, non-narrative logs and transactions into event representations that capture context and support LLM reasoning.A balance update is represented with event context such as the customer, deposit purpose, transfer method, updated field, and timing.Source: §3.2 · p. 3
3.3 Business Modeling Layer: Intent Extraction and Process Structuring
- The Business Modeling Layer extracts goals, entities, constraints, and outcomes, then maps intent to contextualized process templates and machine-readable specifications.Specifications such as BPMN or JSON are passed to CoA for dynamic orchestration while preserving enterprise logic.Source: §3.3 · p. 3
3.4 LLM Integration Layer: Semantic Reasoning and Task Generation
- The LLM Integration Layer turns business intent into structured action specifications and intermediate decisions for downstream agents.It supports goal-conditioned reasoning, fallback planning, and context-aware adaptation beyond deterministic rule matching.Source: §3.4 · p. 4
3.5 Chain of Actions (CoA) Execution Engine: Dynamic Multi-Agent Orchestration
- CoA decomposes goals into task graphs, maps tasks to agents, schedules dependent work sequentially or in parallel, and handles failures through fallback or human review.It monitors outputs and aggregates them into a final result, supporting runtime adaptability and reusable workflows.Source: §3.5 · p. 4
3.6 Execution and Deployment Layer: Scalable Agent Infrastructure
- The deployment layer provides a runtime environment for scalable, modular, observable workflows and connects generated processes to operational systems through orchestration engines and secure APIs.This integration supports workflow delivery into ERP, CRM, and financial platforms, with KPI-level monitoring and reporting.Source: §3.6 · p. 4
4 Performance Evaluation
The evaluation describes GBPA integration with core banking systems and an intent-driven wire-transfer workflow, then assesses the framework on wire transfers and employee reimbursements. The case studies report faster processing and fewer errors after workflow redesign.
4.1 System-Level Integration: GBPAs in Core Banking Architecture
- Core banking integration links front-office credential checks and metadata extraction to workflow generation, business processing, and logged core-module operations.The architecture includes GPBA modules for dynamic process generation, optimization, rule enforcement, LLM orchestration, and visualization.Source: §4.1 · p. 5
4.2 Workflow Example: AI-Driven Wire Transfer Execution
- Customer intent triggers an LLM reasoning pipeline that coordinates specialized agents for wire-transfer assessment, execution, and post-transaction queries.The workflow spans data access and action across traditionally siloed modules.Source: §4.2 · p. 5
4.3 Experiment Design: Comparative Evaluation Across Two Workflows
- The evaluation uses wire transfers as a structured, compliance-driven case and reimbursements as a semi-structured, policy-driven case.Wire transfers test high-stakes requirements such as SWIFT integration and AML/KYC screening; reimbursements include unstructured receipts, discretionary approvals, and human review.Source: §4.3 · p. 5
4.4 Case Study 1: Optimizing Bank Wire Transfers with GBPAs
- Average wire-transfer processing time fell from 15 to 9 minutes (40% improvement), while inter-node wait time dropped by 57%.Independent compliance, account, and identity checks ran in parallel, and purpose verification was added as a risk control.Source: §4.4 · p. 6, Table 2 · p. 7
4.5 Case Study 2: Optimizing Reimbursement Process with GBPAs
- 82% end-to-end time reduction and 94% error-rate reduction were reported for reimbursement workflows after GBPA optimization.The redesign reduced the workflow from 5 sequential steps to 3 stages and expanded risk checkpoints from 1 to 3.Source: §4.5 · p. 6, Table 3 · p. 7
5 Conclusion
The conclusion presents GBPAs as an AI-native ERP framework combining LLM reasoning, modular agent orchestration, and structured business modeling. Real-world financial evaluations are reported to improve efficiency, accuracy, and compliance.
5 Conclusion
- GBPAs combine LLM reasoning, modular agent orchestration, and structured business modeling for ERP automation.The paper describes the framework as an AI-native architecture.Source: §5 · p. 7
- CoA-based planning and a unified 5W3H1R schema support dynamic, interpretable workflows.Source: §5 · p. 7
- Evaluations on real-world financial scenarios report improvements in efficiency, accuracy, and compliance.The paper presents the framework as a scalable foundation for AI-driven enterprise transformation.Source: §5 · p. 7
6 Discussion and Future Outlook: From AI-Augmented Tools to AI-Native Enterprises
The discussion argues that AI-native enterprises can replace fragmented, human-centric workflows with dynamically composed agent workflows, while making AI literacy a practical organizational capability. It positions finance as a particularly suitable domain for this orchestration.
6.1 From Fragmented Tools to AI-Native Architectures
- Human-function divisions create handoffs, misaligned data, duplicated effort, and inefficiencies; AI-native agents instead dynamically compose workflows and shift people toward strategic oversight.The proposed shift treats data as the primary asset and intelligent agents as the computational fabric.Source: §6.1 · p. 7
6.2 AI Literacy: A New Organizational Capability
- The framework makes AI literacy tangible by letting employees observe how their decisions influence process outcomes through interpretable, auditable, and actionable systems.The paper frames AI literacy as both an individual and organizational capability, and states that Article 4 requires sufficient literacy among personnel responsible for deploying and operating AI tools.Source: §6.2 · p. 7
6.3 Financial Services: A Natural Domain for AI-Native Orchestration
- GBPAs offer a scalable path to AI-native enterprises, particularly in data-driven, compliance-intensive domains such as finance.The paper describes financial services as information-driven, with functions organized around rules, records, and decisions.Source: §6.3 · p. 7