Abstract
Tax evasion is an illegal activity where a taxpayer avoids paying his/her tax liability. Any taxpayer has to file their tax return statements periodically at regular intervals. Avoiding to file or delaying the filing of the tax return statement is one among the most basic methods of tax evasion. The taxpayers who are not filing returns or delaying the filing of returns are called return defaulters. Financial loss to the Government due to avoiding to file or delayed filing of returns varies between taxpayers. While designing any statistical model to predict return defaulters, we have to take into account the real financial loss associated with the misclassification. In this paper, we constructed an example dependent cost - sensitive logistic regression model that predicts whether a taxpayer is a potential return defaulter for the upcoming tax-filing period. While designing the model, we studied the effect of business interactions among the taxpayers on return filing behavior. We developed this model for the commercial taxes department, Government of Telangana, India. Applying our method to tax data, we show significant cost saving.
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Acknowledgment
We would like to express our deep thanks towards the government of Telangana, India, for allowing us to use the Commercial Taxes Data set and giving us constant encouragement and financial support. This work has been supported by Visvesvaraya Ph.D. Scheme for Electronics and IT, Media Lab Asia, grant number EE/2015-16/023/MLB/MZAK/0176.
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Mehta, P., Mathews, J., Kumar, S., Suryamukhi, K., Babu, C.S. (2019). Curtailing the Tax Leakages by Nabbing Return Defaulters in Taxation System. In: Le, T., et al. Data Mining. AusDM 2019. Communications in Computer and Information Science, vol 1127. Springer, Singapore. https://doi.org/10.1007/978-981-15-1699-3_15
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