Machine Learning in Auditing: Problems, Solutions, Guidelines, Future Directions

Xinke Bai

2024

Abstract

In the modern and extremely dynamic business arena, auditing can be seen as the only way of getting financial information clarified, taking individuals who have that responsibility to account, and creating the impression of an honest player. The effectiveness of auditing is not just limited to risk mitigation approaches considering prevention of fraud, as it also strengthens the principles of legal compliance, market order, and economic stability in general. Several factors are propelling the auditing industry through a significant transformation at an increasingly fast rate, and these factors are big data and AI, of which machine learning (ML) is one of them. Traditional audits are made faster and more precision by ML through scrutinizing and interpreting enormous and complex data sets, such as detecting risk and anomaly violations and automatically generating reports. Yet, the auditing activities introduce ML into the auditing processes this creates another problem. These encompass issues of data quality and reliability, models transparency and interpretability for complex systems, risks of overfitting and underfitting, and also include security and moral considerations in the software and hardware regulations. On the other hand, there exists a cognate challenge of the auditors getting upskilled to effectively exploit these AI-driven technologies. This paper seeks to determine the problems of empirical evaluation of ML techniques and suggest possible solutions to maximize the efficiency and productivity of the auditing processes.

Paper Citation

in Harvard Style

Bai X. (2024). Machine Learning in Auditing: Problems, Solutions, Guidelines, Future Directions. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 496-503. DOI: 10.5220/0013269400004568

in Bibtex Style

@conference{ecai24,
author={Xinke Bai},
title={Machine Learning in Auditing: Problems, Solutions, Guidelines, Future Directions},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={496-503},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013269400004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Machine Learning in Auditing: Problems, Solutions, Guidelines, Future Directions
SN - 978-989-758-726-9
AU - Bai X.
PY - 2024
SP - 496
EP - 503
DO - 10.5220/0013269400004568
PB - SciTePress