Enterprise Financial Fraud Early Warning and Risk Assessment Model Based on Machine Learning Algorithm

Xin Yao, Juan Xu

2025

Abstract

Fraud early warning and risk assessment models play an important role in corporate finance, but there is the problem of inaccurate risk positioning. The traditional genetic algorithm cannot solve the problem of early warning evaluation in enterprise finance, and the effect is not satisfactory. In an increasingly complex business environment, businesses face increasing financial risk, with financial fraud being particularly devastating. As technology advances, machine learning algorithms have become a powerful tool for improving businesses' ability to identify potential financial fraud and conduct effective risk assessments. This article will explore the application of machine learning in financial alerting and risk assessment, and highlight its importance in maintaining healthy business operations.

Paper Citation

in Harvard Style

Yao X. and Xu J. (2025). Enterprise Financial Fraud Early Warning and Risk Assessment Model Based on Machine Learning Algorithm. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 373-378. DOI: 10.5220/0013543800004664

in Bibtex Style

@conference{incoft25,
author={Xin Yao and Juan Xu},
title={Enterprise Financial Fraud Early Warning and Risk Assessment Model Based on Machine Learning Algorithm},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={373-378},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013543800004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Enterprise Financial Fraud Early Warning and Risk Assessment Model Based on Machine Learning Algorithm
SN - 978-989-758-763-4
AU - Yao X.
AU - Xu J.
PY - 2025
SP - 373
EP - 378
DO - 10.5220/0013543800004664
PB - SciTePress