Artificial Intelligence Model for Detecting Tax Evasion Involving Complex Network Schemes

Nuryani Nuryani; Achmad Benny Mutiara; I Made Wiryana; Detty Purnamasari; Souza Nurafrianto Windiartono Putra

doi:10.34306/att.v6i3.436

Authors

Nuryani Nuryani Directorate General of Taxation https://orcid.org/0009-0001-2079-5897
Achmad Benny Mutiara Universitas Gunadarma https://orcid.org/0000-0003-0220-8289
I Made Wiryana Universitas Gunadarma
Detty Purnamasari Universitas Gunadarma https://orcid.org/0000-0003-2806-720X
Souza Nurafrianto Windiartono Putra Universität des Saarlandes

DOI:

https://doi.org/10.34306/att.v6i3.436

Keywords:

Artificial Intelligence, Big Data, Machine Learning, Social, Network Analysis

Abstract

Tax evasion through complex network schemes poses a significant challenge to tax authorities, leading to substantial revenue losses. This paper aims to develop and evaluate an artificial intelligence model designed to detect tax evasion within complex corporate networks, providing a comprehensive overview and prediction of tax avoidance behaviors. Employing a systematic literature review and document analysis of applicable tax regulations, the study utilizes Social Network Analysis (SNA) as a primary technique for mapping and analyzing taxpayer networks. The process involves matching taxable identities, constructing taxpayer graphs, extracting features, and developing a machine learning model. The proposed architectures and processes demonstrate the potential for tax authorities to enhance their capabilities in detecting tax evasion involving complex networks, with the machine learning model effectively identifying features related to both individual and network characteristics of taxpayers. The findings suggest that the integration of artificial intelligence and big data analytics can significantly improve the detection of tax evasion in complex corporate structures, offering valuable tools for tax authorities to better enforce tax compliance.

References

J. Ruan, Z. Yan, B. Dong, Q. Zheng, and B. Qian, “Identifying suspicious groups of affiliated-transaction based tax evasion in big data,” Information Sciences, vol. 477, pp. 508–532, 2019.

R. A. Rahman, S. Masrom, and N. Omar, “Tax avoidance detection based on machine learning of malaysian government-linked companies,” International Journal of Recent Technology and Engineering, vol. 8, no. 2, pp. 535–541, 2019.

H. Yu, H. He, Q. Zheng, and B. Dong, “Taxvis: a visual system for detecting tax evasion group,” in The World Wide Web Conference, 2019, pp. 3610–3614.

P. Mehta, J. Mathews, S. Kumar, K. Suryamukhi, C. Sobhan Babu, and S. Kasi Visweswara Rao, “Big data analytics for nabbing fraudulent transactions in taxation system,” in Big Data–BigData 2019: 8th International Congress, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25–30, 2019, Proceedings 8. Springer, 2019, pp. 95–109.

B. Shi, B. Dong, Y. Xu, J. Wang, Y. Wang, and Q. Zheng, “An edge feature aware heterogeneous graph neural network model to support tax evasion detection,” Expert Systems with Applications, vol. 213, p. 118903, 2023.

M.-J. Segovia-Vargas et al., “Detection of shell companies in financial institutions using dynamic social network,” Expert Systems with Applications, vol. 207, p. 117981, 2022.

J. Garcia-Bernardo, J. Witteman, and M. Vlaanderen, “Uncovering the size of the illegal corporate service provider industry in the netherlands: a network approach,” EPJ Data Science, vol. 11, no. 1, p. 23, 2022.

Division for Sustainable Development Goals, Department of Economic and Social Affairs, United Nations, “Sustainable development goals un,” 2024. [Online]. Available: https://sdgs.un.org/goals

The World Bank, “Sustainable development goals sdg,” 2024. [Online]. Available: https: //www.worldbank.org/en/programs/sdgs

——, “Our world in data: The united nations sustainable development goals (sdgs),” 2020. [Online]. Available: https://ourworldindata.org/sdgs

M. R. Anwar and L. D. Sakti, “Integrating artificial intelligence and environmental science for sustainable urban planning,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 2, pp. 179–191, 2024.

C. Stamile, A. Marzullo, and E. Deusebio, Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms. Packt Publishing Ltd, 2021.

E. Scifo, Graph Data Science with Neo4j: Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project. Packt Publishing Ltd, 2023.

N. Anwar, A. M. Widodo, B. A. Sekti, M. B. Ulum, M. Rahaman, and H. D. Ariessanti, “Comparative analysis of nij and nist methods for microsd investigations: A technopreneur approach,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 2, pp. 169–181, 2024.

G. S. Putra, I. I. Maulana, A. D. Chayo, M. I. Haekal, R. Syaharani et al., “Pengukuran efektivitas platform e-learning dalam pembelajaran teknik informatika di era digital,” Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi, vol. 3, no. 1, pp. 19–29, 2024.

J. Lismont, E. Cardinaels, L. Bruynseels, S. De Groote, B. Baesens, W. Lemahieu, and J. Vanthienen, “Predicting tax avoidance by means of social network analytics,” Decision Support Systems, vol. 108, pp. 13–24, 2018.

J. Jones, E. Harris, Y. Febriansah, A. Adiwijaya, and I. N. Hikam, “Ai for sustainable development: Applications in natural resource management, agriculture, and waste management,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 143–149, 2024.

M. Pereira, I. Guvlor et al., “Implementation of artificial intelligence framework to enhance human resources competency in indonesia,” International Journal of Cyber and IT Service Management, vol. 4, no. 1, pp. 64–70, 2024.

H. A. Winata and F. Simon, “Influence of profitability, audit quality, and corporate governance on earnings management,” APTISI Transactions on Management, vol. 8, no. 2, pp. 93–104, 2024.

S. Lestari, S. Watini, and D. E. Rose, “Impact of self-efficacy and work discipline on employee performance in sociopreneur initiatives,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 2, pp. 270–284, 2024.

A. Ruangkanjanases, A. Khan, O. Sivarak, U. Rahardja, and S.-C. Chen, “Modeling the consumers’ flow experience in e-commerce: The integration of ecm and tam with the antecedents of flow experience,” SAGE Open, vol. 14, no. 2, p. 21582440241258595, 2024.

D. Nugroho and P. Angela, “The impact of social media analytics on sme strategic decision making,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 2, pp. 169–178, 2024.

Y. S. Dewi, “Influence of type and dose of coagulants on vehicle wash wastewater,” ADI Journal on Recent Innovation, vol. 6, no. 1, pp. 8–16, 2024.

U. Rahardja, N. Lutfiani et al., “The strategy of improving project management using indicator measurement factor analysis (imf) method,” in Journal of Physics: Conference Series, vol. 1477, no. 3. IOP Publishing, 2020, p. 032023.

L. W. Ming, J. Anderson, F. Hidayat, F. D. Yulian, and N. Septiani, “Ai as a driver of efficiency in waste management and resource recovery,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 128–134, 2024.

D. Bennet, S. A. Anjani, O. P. Daeli, D. Martono, and C. S. Bangun, “Predictive analysis of startup ecosystems: Integration of technology acceptance models with random forest techniques,” CORISINTA, vol. 1, no. 1, pp. 70–79, 2024.

U. Rahardja, I. D. Hapsari, P. H. Putra, and A. N. Hidayanto, “Technological readiness and its impact on mobile payment usage: A case study of go-pay,” Cogent Engineering, vol. 10, no. 1, p. 2171566, 2023.

D. S. Wuisan and T. Handra, “Maximizing online marketing strategy with digital advertising,” Startupreneur Business Digital (SABDA Journal), vol. 2, no. 1, pp. 22–30, 2023.

P. A. Sunarya, R. Refianti, A. B. Mutiara, and W. Octaviani, “Comparison of accuracy between convolutional neural networks and na¨ıve bayes classifiers in sentiment analysis on twitter,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 5, 2019.

S. A. Hasan, W. N. Al-Zahra, A. S. Auralia, D. A. Maharani, R. Hidayatullah et al., “Implementasi teknologi blockchain dalam pengamanan sistem keuangan pada perguruan tinggi,” Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi, vol. 3, no. 1, pp. 11–18, 2024.

E. Ligia, K. Iskandar, I. K. Surajaya, M. Bayasut, O. Jayanagara, and K. Mizuno, “Cultural clash: Investigating how entrepreneural characteristics and culture diffusion affect international interns’ competency,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 2, pp. 182–198, 2024.

P. Sithole, E. Zirolla, and S. Lowel, “Artificial intelligence in literacy libraries a review of the literature,” International Journal of Cyber and IT Service Management, vol. 4, no. 1, pp. 58–63, 2024.

V. Melinda, T. Williams, J. Anderson, J. G. Davies, and C. Davis, “Enhancing waste-to-energy conversion efficiency and sustainability through advanced artificial intelligence integration,” International Transactions on Education Technology (ITEE), vol. 2, no. 2, pp. 183–192, 2024.

D. Hernandez, L. Pasha, D. A. Yusuf, R. Nurfaizi, and D. Julianingsih, “The role of artificial intelligence in sustainable agriculture and waste management: Towards a green future,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 150–157, 2024.

C. Lukita, L. D. Bakti, U. Rusilowati, A. Sutarman, and U. Rahardja, “Predictive and analytics using data mining and machine learning for customer churn prediction,” Journal of Applied Data Sciences, vol. 4, no. 4, pp. 454–465, 2023.

M. F. Nur and A. Siregar, “Exploring the use of cluster analysis in market segmentation for targeted advertising,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 2, pp. 158–168, 2024.

S. Audiah, Y. P. A. Sanjaya, O. P. Daeli, M. Johnson et al., “Transforming energy and resource management with ai: From theory to sustainable practice,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 158–163, 2024.

E. Sana, A. Fitriani, D. Soetarno, M. Yusuf et al., “Analysis of user perceptions on interactive learning platforms based on artificial intelligence,” CORISINTA, vol. 1, no. 1, pp. 26–32, 2024.

T. Hidayat, D. Manongga, Y. Nataliani, S. Wijono, S. Y. Prasetyo, E. Maria, U. Raharja, I. Sembiring et al., “Performance prediction using cross validation (gridsearchcv) for stunting prevalence,” in 2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS). IEEE, 2024, pp. 1–6.

B. Any, S. Four, and C. Tariazela, “Technology integration in tourism management: Enhancing the visitor experience,” Startupreneur Business Digital (SABDA Journal), vol. 3, no. 1, pp. 81–88, 2024.

N. Lutfiani, N. P. L. Santoso, R. Ahsanitaqwim, U. Rahardja, and A. R. A. Zahra, “Ai-based strategies to improve resource efficiency in urban infrastructure,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 121–127, 2024.

J. L. Willson, A. Nuche, and R. Widayanti, “Ethical considerations in the development of ai-powered healthcare assistants,” International Transactions on Education Technology (ITEE), vol. 2, no. 2, pp. 109–119, 2024.

P. A. Sunarya, U. Rahardja, S. C. Chen, Y.-M. Lic, and M. Hardini, “Deciphering digital social dynamics: A comparative study of logistic regression and random forest in predicting e-commerce customer behavior,” Journal of Applied Data Sciences, vol. 5, no. 1, pp. 100–113, 2024.

S. R. P. Junaedi, D. Edmond et al., “Successful digital marketing techniques for business development,” Startupreneur Business Digital (SABDA Journal), vol. 3, no. 1, pp. 19–25, 2024.

M. Mohanty, S. R. Jena, and S. K. Misra, “Mathematical modelling of engineering problems,” Journal homepage: http://iieta. org/journals/mmep, vol. 8, no. 3, pp. 409–417, 2021.

N. Lutfiani, A. Ivanov, N. P. L. Santoso, S. V. Sihotang, and S. Purnama, “E-commerce growth plan for msmes’ sustainable development enhancement,” CORISINTA, vol. 1, no. 1, pp. 80–86, 2024.

K. Mirdad, O. P. M. Daeli, N. Septiani, A. Ekawati, and U. Rusilowati, “Optimizing student engagement and performance usingai-enabled educational tools,” CORISINTA, vol. 1, no. 1, pp. 53–60, 2024.

C. davis Davis et al., “Artificial intelligence in education: Enhancing learning experiences through personalized adaptation,” International Journal of Cyber and IT Service Management, vol. 4, no. 1, pp. 26–32, 2024.

U. Rahardja, A. Sari, A. H. Alsalamy, S. Askar, A. H. R. Alawadi, and B. Abdullaeva, “Tribological properties assessment of metallic glasses through a genetic algorithm-optimized machine learning model,” Metals and Materials International, vol. 30, no. 3, pp. 745–755, 2024.

S. Edilia and N. D. Larasati, “Innovative approaches in business development strategies through artificial intelligence technology,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 1, pp. 84–90, 2023