An Enhanced Predictive Analytics Model for Tax-Based Operations
DOI:
https://doi.org/10.31436/ijpcc.v9i1.343Keywords:
Taxes, Tax operation, Predictive Analytics, OptimisationAbstract
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 parallelisation in function-based classifiers to discover relationships that were initially non-existent
References
A. D. Zubairu. Understanding Taxation. Nassarawa: Press, 2011..
J. Abiola. & M. Asiweh. Impact of tax administration on government revenue in a developing economy-a case study of Nigeria. International Journal of business and social science, 3(8), 2012.
S. Taddesse. Taxation for Sustained Prosperity: Collaborative Tax Policy Setting & Administration. Bublish, 2022, Inc.
D. Desiderio. Data analysis techniques for enhancing the performance of Customs. World Customs Journal, 13(2), 2019, pp.17-22.
M. Önder, & U.N. Zengin. A framework for comparative analysis: Public administration across the globe. In The Palgrave Handbook of Comparative Public Administration (pp. 51-76). 2022, Palgrave Macmillan, Singapore.
T. Connolly, & C. Begg. Database Systems. A Practical Approach to Design, Implementation and Management. Harlow: 2015, Addisson Wesley.
P. Simon. Too Big to Ignore: The Business Case for Big Data. 2013, Wiley.
T.M. Mitchell. Machine Learning. 1995, McGraw-Hill
C.S. Lee, Cheang, P.Y.S. and M. Moslehpour. Predictive analytics in business analytics: decision tree. Advances in Decision Sciences, 26(1), 2022, pp.1-29.
E.Z. Ferdousy, M.M. Islam & M. Abdul Matin. Combination of Naive Bayes Classifier and K-Nearest Neighbour (cNK). Computer and Information Science, 6, 2013, 48-56.
M. Kachele, G. Palm, G., & F. Schwenker. SMO Lattices for the Parallel Training of Support Vector Machines. European Symposium on Artificial Neural Network (pp. 579-584), 2015, Ulm: Bruges.
S. Singh. Digital Transformation in the Tax World. Tax Executive, 2022, 74, p.64.
Junquera-Varela, R.F., Lucas, C.O., Krsul, I., Calderon Yksic, V.O. and Arce Rodriguez, P., 2022. Digital Transformation of Tax and Customs Administrations.
J. Owens, B. Schlenther, K.S. Adjeyi, K.E. Amaglo, T. Kouevi, A. Jansen, O.K. Mackenzie. Steyn, W., Tchila, P., Okey, M.K.N. and N. Traore. Developments in the Use of Technologies in African Tax Administrations. African Multidisciplinary Tax Journal, 2022(1), pp.1-22.
N. Noor, A. Sarlan, and N. Aziz. Revenue Prediction for Malaysian Federal Government Using Machine Learning Technique. In 2022 11th International Conference on Software and Computer Applications, 2022, pp. 143-148.
J. Choe, A. Crotty. T. Moreshet. M. Herlihy,. and R.I. Bahar.. HybriDS: Cache-Conscious Concurrent Data Structures for Near-Memory Processing Architectures. In Proceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures, 2022, pp. 321-332.
Z. Chu, P. Xiao, P., Mi, D., Hao, W., Khalily, M. and L.L. Yang.. A novel transmission policy for intelligent reflecting surface assisted wireless powered sensor networks. IEEE Journal of Selected Topics in Signal Processing, 15(5), 2021, pp.1143-1158.
J. Butler. Big Data and Analytics at the IRS. Proceedings of IBM-TechAmerica , 6, 2012.
P.C. Gonzalez, & J.D. Velasquez. Characterization and detection of tax payers with false invoices using data mining techniques. Expert Systems with Applications , 1. 2013.
