arXiv:2606.03031v1 Announce Type: new Abstract: Structured financial audit verification is difficult for language-model agents because correctness depends on structured evidence rather than text alone. A model must link reported facts to taxonomy concepts, traverse calculation or dimensional relations, and recompute expected values before applying an audit rule. We propose AuditFlow, a graph-grounded multi-agent framework that separates adaptive search from deterministic verification. AuditFlow builds a symbolic environment from a static US-GAAP taxonomy graph and a dynamic XBRL filing graph,
The increasing sophistication of language models clashes with the structured, evidence-based requirements of financial reporting, necessitating new architectures for reliable AI application in audit.
This development addresses a critical gap in applying AI to highly regulated, structured data environments, enabling automation of complex financial verification tasks that demand precision and traceability.
AI agents will be able to perform structured financial reporting verification by combining symbolic reasoning with adaptive search, moving beyond text-based analysis to evidence-grounded computations.
- · Financial auditing firms
- · Regulatory bodies
- · Enterprise software providers
- · AI agents developers
- · Manual financial verification processes
- · Legacy unstructured data analysis tools
- · Companies with poor financial data hygiene
Audit processes become faster, more accurate, and less labor-intensive, reducing costs and increasing compliance.
The improved reliability of AI in financial verification could lead to broader AI adoption in other structured, high-stakes domains.
Enhanced audit capabilities could fundamentally alter regulatory oversight, allowing for more frequent and comprehensive financial scrutiny across industries.
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Read at arXiv cs.AI