Application and Exploration of Artificial Intelligence Technology in Audit Risk Identification

Abstract

Audit risk identification is a core task in the audit planning stage, and its results directly affect the accuracy and reliability of audit conclusions. Under the traditional audit model, constrained by tight timelines and limited technical means, auditors often have difficulty mining and verifying massive volumes of raw data in a timely and comprehensive manner. However, with the powerful natural language processing capabilities of large language models (LLMs) and the automated execution features of intelligent agents (Agents), auditors are now able to quickly filter and deeply analyze various structured and unstructured data, accurately identify key audit risks, and further improve and systematically construct the audit evidence chain. This paper focuses on analyzing the collaborative working mechanism of LLM and Agent technologies in audit risk identification, designs an intelligent audit risk identification methodology that integrates LLM and Agent, and discusses its application value in multi-source data analysis, evidence chain construction, and other aspects.