Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023)

Anomaly detection in Financial Data

Authors
Yetong Li1, *
1School of Management, Wuhan University of Technology, Wuhan, 430070, China
*Corresponding author. Email: 309076@whut.edu.cn
Corresponding Author
Yetong Li
Available Online 10 October 2023.
DOI
10.2991/978-94-6463-268-2_46How to use a DOI?
Keywords
accounting; auditing; financial data
Abstract

The business world is a colorful and diverse world in which the integration and communication of many fields will be involved. In order to explore this world, it is necessary to master the basic language of this world - financial statements; however, the prerequisite for the stable operation of the business world is that the financial data of each company are in legal, fair and objective. These fraudulent practices can have multiple effects, which may affect the rights and interests of stakeholders such as consumers, investors and even the entire business world, which requires the presence of auditors, but they need to invest a lot of manpower to deal with the complex and laborious work. find some detection algorithms that can help auditors to increase the efficiency of their work while increasing the detection of financial data sets.

Copyright
© 2024 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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Volume Title
Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023)
Series
Advances in Economics, Business and Management Research
Publication Date
10 October 2023
ISBN
10.2991/978-94-6463-268-2_46
ISSN
2352-5428
DOI
10.2991/978-94-6463-268-2_46How to use a DOI?
Copyright
© 2024 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.

Cite this article

TY  - CONF
AU  - Yetong Li
PY  - 2023
DA  - 2023/10/10
TI  - Anomaly detection in Financial Data
BT  - Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023)
PB  - Atlantis Press
SP  - 419
EP  - 426
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-268-2_46
DO  - 10.2991/978-94-6463-268-2_46
ID  - Li2023
ER  -