An enhanced predictive analytics model for tax-based operations
In order to meet its basic responsibilities of governance such as provision of infrastructure, governments world over require significant amount of funds. Consequently, citizens and businesses are required to pay certain legislated amounts as taxes and royalties. However, tax compliance and optima...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
IIUM Press
2023
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/106608/7/106608_An%20enhanced%20predictive%20analytics%20model%20for%20tax-based%20operations.pdf http://irep.iium.edu.my/106608/ https://journals.iium.edu.my/kict/index.php/IJPCC/article/view/343/215 https://doi.org/10.31436/ijpcc.v9i1.343 |
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| Summary: | In order to meet its basic responsibilities of governance such as provision of infrastructure,
governments world over require significant amount of funds. Consequently, citizens and businesses are
required to pay certain legislated amounts as taxes and royalties. However, tax compliance and optimal
revenue generation remains a major source of concern. Measures such as penalties and in the current
times Data and Predictive Analytics have been devised to curb these issues. Such effective Analytics
measures are absent in Bauchi State and Nigeria as a whole. Previous studies in Nigeria have done much
in the area of tax compliance but have not implemented Data Analytics solutions to unearth the
relationships which this study will cover. A Combined Sequential Minimal Optimisation (CSMO) model
has been developed to analyse co-relation of Tax-payers, classification and predictive traits which
uncovers trends on which to base overall decisions for the ultimate goal of revenue generation.
Experimental validation demonstrates the advantages of CSMO in terms of classification, training time
and prediction accuracy in comparison to Sequential Minimal Optimisation (SMO) and Parallel Sequential
Minimal Optimisation (PSMO). CSMO recorded a Kappa Statistics measure of 0.916 which is 8% more
than the SMO and 7.8% more than the PSMO; 99.74% correctly classified instances was compared to
98.28% in SMO and 98.35 in parallel SMO. Incorrectly classified instances of CSMO recorded a value of
0.25% which is better than 1.72% of SMO and 1.68% of PSMO. Training time of 223ms was recorded when
compared to 378ms in SMO and 286ms in PSMO. A better value of 0.9981 for CSMO was achieved in the
ROC Curve plot against 0.944 in SMO and 0.913 in PSMO. CSMO takes advantage of powerful Analytics
techniques such as prediction and parallelization in function-based classifiers to discover relationships
that were initially non-existent |
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