Implementation of Artificial Neural Networks as a Method for Early Detection of Tax Evasion Behavior in Indonesia - Nataliawati, Rita; Yaumi , Sri; Qosim; Yulianita Kusumawati , Feni; Sutiani (Accounting and Finance Studies, 2024)
Research Aims: This study aims to analyze the ability of the Artificial Neural Network (ANN) model in early detection of tax evasion behavior in Indonesia. Design/methodology/approach: This study uses a qualitative method with a descriptive approach, the types of data used in this study are primary data and secondary data. Primary data is obtained from the results of interviews to obtain the information needed by the researcher. The interview informants came from the DGT sub-Directorate of Potential, Compliance and Tax Revenue with the initials K&H, tax consultants and taxpayers. while secondary data is collected through the DGT online website and through articles and journals on Artificial Intelligence (AI), Artificial Neural Networks (ANN), tax evasion, and financial information data on income, expenses, amount of assets, taxes payable) and behavioral indicators (tax compliance history, frequency of late reporting, significant changes in financial statements). The data analysis technique used is descriptive analysis, namely data collection, data reduction, data presentation and conclusion drawing   Research Findings: The results of this study show that the use of a neural network model can be an effective tool to detect symptoms of tax evasion behavior early. This method allows tax authorities to monitor and identify suspicious activity more efficiently and effectively. Theoretical Contribution/Originality: This study makes a significant theoretical contribution by integrating the use of Artificial Neural Networks (ANN) in detecting tax evasion in Indonesia. The originality of this research lies in the application of ANN in the context of Indonesia's tax system, where the use of advanced technology such as ANN is still relatively new. The study also combines the pentagon's fraud theory, which adds elements of arrogance and competence in the analysis of tax evasion behavior, with a machine learning approach, creating a more holistic and innovative framework