Multi-Agent Framework for Audit Risk Assessment with Explicit Uncertainty and Evidence Conflict Modeling
arXiv:2606.15640v1 Announce Type: new Abstract: Audit risk assessment increasingly benefits from combining heterogeneous evidence sources, yet existing approaches typically produce point predictions without quantifying how well different evidence streams agree. We propose UMAR (Uncertainty-Aware Multi-Agent Risk Assessment), a framework that employs three specialized agents: an MD&A Text Agent, a Financial Ratio Agent, and a CAM Agent, each producing independent risk scores with calibrated uncertainty estimates. An Uncertainty Aggregator based on Dempster-Shafer evidence theory fuses these sco
The increasing complexity of financial data and the drive for greater efficiency and accuracy in audit processes are pushing for advanced AI applications.
This development allows for more robust and reliable audit risk assessments by explicitly quantifying uncertainty and resolving conflicts from diverse evidence, directly impacting financial integrity and regulatory compliance.
Auditors can now leverage AI to not only generate risk scores but also understand the reliability and agreement among different analytical agents, moving beyond simple point predictions.
- · Audit firms
- · Regulatory bodies
- · Companies seeking accurate financial assessments
- · AI-driven financial technology providers
- · Traditional manual audit processes
- · Companies relying on less sophisticated risk assessment tools
Improved accuracy and efficiency in financial auditing through AI-driven multi-agent systems.
Increased investor confidence due to more transparent and rigorously assessed financial statements.
Potential for new regulatory standards that mandate or highly recommend AI-powered audit risk assessments.
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Read at arXiv cs.LG