Predicting Firms’ Taxpaying Behaviour Using Artificial Neural Networks: The Case of Indonesia
53 Pages Posted: 30 Aug 2022
Date Written: August 9, 2022
Due to the complexity of tax and the time and resources needed to monitor and examine tax returns, tax noncompliance is challenging to detect. Big data and sophisticated analytics might help tax authorities extract actionable data insights. Using income tax record data, this paper employs an Artificial Neural Networks (ANN) model to predict and discover the determinants of firms’ taxpaying behaviour. To the best of the author’s knowledge, this study is the first to apply ANN to exploit the taxpaying behaviour of Indonesian firms. This work examined 538,254 firm-level administrative data across fiscal years 2014 and 2019 to predict the magnitude of tax payment based on seven variables of interest: types of tax returns, gross profit margin, operating profit margin, other business income ratio, other business expense ratio, positive fiscal adjustment ratio, and negative fiscal adjustment ratio. Multi-Layer Perceptron Neural Network-based models were trained to predict three categories of taxpaying measurement—i.e, Corporate Tax Turnover Ratio (CTTOR)—across varying magnitudes of annual turnover. The models predicted the firms' taxpaying behaviour with an average accuracy rate above 92%. The implementation of artificial intelligence also allows this study to identify heterogeneous channels responsible for firms’ taxpaying behaviour across groups. This study finds other business income and positive fiscal adjustment to be significant predictors of taxpaying behaviour for small and medium firms. In contrast, operating profit margin, other business expenses, and negative fiscal adjustment are prominent predictors for large corporations. The findings will assist decision-makers in tax administrations about potential areas of misreporting, enabling them to develop evidence-based and effective policy actions.
Keywords: corporate taxpayers, tax compliance, taxpaying behaviour, artificial neural networks
JEL Classification: D22, H25, H26, H32
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