Beyond detection: A multi-agent framework for root cause analysis of financial discrepancies in distributed environment

The increasing complexity and fragmentation of financial systems in large organizations have created significant challenges for financial teams, particularly in performing real-time, end-to-end validation, as existing validation methods relying on static rules or batch processing are often inadequate for today’s dynamic financial environments. This paper introduces a novel approach using Large Language Model (LLM)-based browser agents within a multiagent framework to enhance financial validation processes. The framework leverages domain-specific agents that autonomously navigate web-based financial platforms to validate data, interpret discrepancies, and perform root cause analysis, ensuring higher accuracy, transparency, and auditability compared to traditional systems. A synthetic dataset and controlled simulation environment were used to evaluate the framework’s performance across 20 distinct financial scenarios, revealing significant improvements in validation accuracy (from 40% with a Vanilla agent to 65% with the proposed approach). The results indicate that the proposed multi-agent approach, by isolating validation tasks into specialized agents and orchestrating a coordinated investigation, provides a more reliable, scalable, and interpretable solution for high-stakes financial environments