Research on the Application of Intelligent Decision-Making and Machine Learning in Auditing

DOI

10.2991/978-94-6463-980-3_25How to use a DOI?

Keywords

Intelligent Decision-Making; Machine Learning; Audit Digitalization; Risk Identification; Full-Process Application

Abstract

Amid the wave of digital transformation, auditing faces multiple challenges including surging data volumes, increasingly complex operational scenarios, and heightened oversight requirements. Traditional audit models reliant on manual expertise struggle to meet demands for high-quality development. Intelligent decision-making centers on data-driven approaches, integrating multidimensional information to generate optimal solutions; Machine learning, as a core branch of artificial intelligence, enables algorithmic models to uncover data patterns and achieve autonomous learning. The synergy between these two technologies provides critical support for overcoming bottlenecks in auditing. This paper systematically outlines the technical foundations and implementation details of intelligent decision-making and machine learning, analyzes their application pathways across the entire audit process— “pre-audit preparation, on-site execution, report generation, and corrective action tracking”—and offers references for audit digital transformation that combine theoretical depth with practical value.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xing Zhang
AU  - Hong Liu
PY  - 2025
DA  - 2025/12/26
TI  - Research on the Application of Intelligent Decision-Making and Machine Learning in Auditing
BT  - Proceedings of the 2025 5th International Conference on Business Administration and Data Science (BADS 2025)
PB  - Atlantis Press
SP  - 270
EP  - 278
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-980-3_25
DO  - 10.2991/978-94-6463-980-3_25
ID  - Zhang2025
ER  -