A Machine Learning Approach to Identifying Suspicious Tax Evasion Behavior in Public Financial Data
Issued Date
2023-01-01
Resource Type
Scopus ID
2-s2.0-85164297210
Journal Title
2023 8th International Conference on Business and Industrial Research, ICBIR 2023 - Proceedings
Start Page
1152
End Page
1158
Rights Holder(s)
SCOPUS
Bibliographic Citation
2023 8th International Conference on Business and Industrial Research, ICBIR 2023 - Proceedings (2023) , 1152-1158
Suggested Citation
Visitpanya N., Samanchuen T. A Machine Learning Approach to Identifying Suspicious Tax Evasion Behavior in Public Financial Data. 2023 8th International Conference on Business and Industrial Research, ICBIR 2023 - Proceedings (2023) , 1152-1158. 1158. doi:10.1109/ICBIR57571.2023.10147479 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/87983
Title
A Machine Learning Approach to Identifying Suspicious Tax Evasion Behavior in Public Financial Data
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
The protection of sensitive tax data poses a challenge for data processors seeking to use it freely, particularly in the realm of financial and tax data. Detecting suspicious behavior related to tax evasion in this area requires sophisticated techniques. This paper presents a study on detecting tax evasion behavior using publicly available financial data. The data was obtained from a public source, and machine learning was applied to identify suspicious behavior based on criteria derived from a literature review. Our study yielded a precision of over 97 % using selected supervised models. Future work involves exploring behavior using unsupervised methods, increasing the number of instances using data synthesis techniques, and comparing results with enterprises that are not registered in a stock market as long as the data is legally available. This research provides valuable insights into identifying suspicious tax evasion behavior and can inform the development of improved tax evasion detection systems in the future.