DeepCatch: Predicting Return Defaulters in Taxation System using Example-Dependent Cost-Sensitive Deep Neural Networks

Mehta, Priya and Babu, Ch. Sobhan and Visweswara Rao, S. V. Kasi and Kumar, Sandeep (2020) DeepCatch: Predicting Return Defaulters in Taxation System using Example-Dependent Cost-Sensitive Deep Neural Networks. In: Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, 10 December 2020 - 13 December 2020.

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Abstract

Tax evasion is most common in several nations. Taxpayers evade tax by using thoughtful and well-considered techniques, which hinders the economic progress of the nation. Delaying the filing of returns by taxpayers is the most primitive form of tax evasion. Taxpayers who delay the filing of returns are called return defaulters. It is the most brazen form of tax evasion. To tackle this problem, we introduce an example-dependent cost-sensitive deep learning model to identify potential return defaulters. This model takes example-dependent costs into account and makes predictions that aim to minimize the overall cost instead of minimizing the total number of misclassifications. Applying our method, we show cost savings of about 55%. This work is designed and implemented for the Commercial Taxes Department Government of Telangana, India.

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IITH Creators:

IITH Creators	ORCiD
Mehta, Priya	UNSPECIFIED
Babu, Ch. Sobhan	UNSPECIFIED

Item Type:

Conference or Workshop Item (Paper)

Uncontrolled Keywords:

Cost saving; Cost-sensitive; Economic progress; Learning models; Misclassifications; Overall costs; Tax evasions;Big data; Deep learning; Deep neural networks; Neural networks

Subjects:

Computer science

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

17 Jul 2021 09:07

Last Modified:

17 Jul 2021 09:07

URI:

http://raiithold.iith.ac.in/id/eprint/8397