O Uso de Inteligência Artificial no Combate à Evasão Fiscal: Uma Revisão Sistemática da Literatura
Resumo
Evasão fiscal é um problema enfrentado pelos governos de todo mundo. O uso de IA tem sido uma alternativa viável para combater esse problema. Neste contexto, esse trabalho tem como objetivo identificar como a IA auxilia no combate à evasão fiscal. Para isso, foi desenvolvido uma revisão sistemática da literatura, encontrando 738 artigos, dos quais 18 foram selecionados para esta revisão. A revisão investiga os tipos de algoritmos utilizados, a forma de validação dos modelos e os desafios encontrados durante as pesquisas. Os resultados demonstram um panorama geral das últimas pesquisas desta área, as lacunas nas pesquisas atuais e classificação das técnicas utilizadas recentemente.
Palavras-chave:
Evasão Fiscal, Detecção de Fraude Fiscal, Revisão Sistemática da Literatura
Referências
Adamov, A. Z. (2019) “Machine Learning and Advanced Analytics in Tax Fraud Detection”, 2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT), Baku, Azerbaijan, pp. 1-5. D3
Baesens, B., Vlasselaer V. V. and Verbeke W. (2015) “Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection”. New Jersey: John Wiley & Sons. D3
de Roux, D.; Pérez, B.; Moreno, A.; Villamil, P. and Figueroa, C. (2018). “Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach”. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18), Londor, UK, pp. 2015-222. D3
Dias, M. and Becker, K. (2017). “Identificação de Candidatos à Fiscalização por Evasão do Tributo ISS”. In: 5th Symposium on Knowledge Discovery, Mining and Learning, Uberlândia, MG, 2017. D3
Didimo, W., Grilli, L., Liotta, G., Menconi, L., Montecchiani, F. and Pagliuca D. (2020) “Combining Network Visualization and Data Mining for Tax Risk Assessment”, in IEEE Access, vol. 8, pp. 16073-16086. D3
Dybå, T. and Dingsøyr, T. (2008). “Empirical studies of agile software development: A systematic review”. Information and Software Technology. vol. 50, pp. 833-859. D3
Goumagias, N., Hristu-Varsakelis, D., and Assael, Y. (2018). “Using Deep Q-learning to understand the tax evasion behavior of risk-averse firms”. Expert Systems with Applications, vol. 101, pp. 258-270. D3
Jupri, M. and Sarno R. (2018) “Taxpayer compliance classification using C4.5, SVM, KNN, Naive Bayes and MLP”. 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, pp. 297-303. D3
Kitchenham, B. and Charters, S. (2007) “Guidelines for performing systematic literature reviews in software engineering”. D3
Kleanthous, C., and Chatzis, S.P. (2019). “Gated Mixture Variational Autoencoders for Value Added Tax audit case selection”. Knowledge-Based Systems, vol. 188, 105048. D3
Lismont, J., Cardinaels, E., Bruynseels, L., Groote, S.D., Baesens, B., Lemahieu, W., and Vanthienen, J. (2018). “Predicting tax avoidance by means of social network analytics”. Decision Support Systems, vol. 108, pp. 13-24. D3
Mathews, J., Mehta, P., Kuchibhotla, S., Bisht, D., S. Chintapalli , B. and Rao S. V. K. V.(2018) “Regression Analysis towards Estimating Tax Evasion in Goods and Services Tax”, 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Santiago, pp. 758-761. D3
Matos, T., Macedo, J. A., Lettich, F., Monteiro, J. M., Renso, C., Perego and R., Nardini, F. M. (2019). “Leveraging Feature Selection To Detect Potential Tax Fraudsters”. Expert Systems with Applications. vol. 145, 113128. D3
Matos, T.; Macedo, J. A. and Monteiro, J. M. (2015) “An Empirical Method for Discovering Tax Fraudsters”. In: IDEAS '15: Proceedings of the 19th International Database Engineering & Applications Symposium, Yokohama, Japão, p. 41-48. D3
Mehta, P., Mathews, J., Rao, S. V. K.V., Kumar, K. S., Suryamukhi , K., Babu C. S. (2019) “Identifying Malicious Dealers in Goods and Services Tax”, 2019 IEEE 4thInternational Conference on Big Data Analytics (ICBDA), Suzhou, China, pp. 312-316. D3
Mehta, P., Mathews, J., Suryamukhi, K., Kumar, K. S. and Babu, C. S. (2018) “Predictive Modeling for Identifying Return Defaulters in Goods and Services Tax”, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, pp. 631-637. D3
Okoli, C., Schabram, K. (2010) “A Guide to Conducting a Systematic Literature Review of Information Systems Research,” . Sprouts: Working Papers on Information Systems, 10(26). D3
Ouzzani M., Hammady H., Fedorowicz Z. and Elmagarmid A. (2016) “Rayyan-a Web and Mobile App for Systematic Reviews”. Syst Rev. 2016; 5(1): 210. D3
Ying, Q., Xiaoxin, H. and Weige, J. (2019). “Research on Tax Inspection Case Selection Model Based on Bayesian Network”. In Proceedings of the 2019 2nd International Conference on Information Management and Management Sciences (IMMS 2019). Association for Computing Machinery, New York, NY, USA, pp. 198–202. D3
Rahimikia, E., Mohammadi, S., Rahmani, T. and Ghazanfari, M. (2017). “Detecting corporate tax evasion using a hybrid intelligent system: A case study of Iran”. International Journal of Accounting Information Systems. Vol. 25, pp. 1-17. D3
Ruan, J., Yan, Z., Dong, B., Zheng, Q., Qian, B. (2018). “Identifying Suspicious Groups of Affiliated-Transaction-based Tax Evasion in Big Data”. Information Sciences, vol. 477, pp. 508-532. D3
Vanhoeyveld, J., Martens, D., Peeters, B. (2020). “Value-added tax fraud detection with scalable anomaly detection techniques”. Applied Soft Computing, vol. 86, 105895. D3
Wei, R., Dong, B., Zheng, Q., Zhu, X., Ruan, J. and He, H. (2019). “Unsupervised Conditional Adversarial Networks for Tax Evasion Detection”. 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, pp. 1675-1680. D3
Wu, Y., Zheng, Q., Gao, Y., Dong, B., Wei, R., Zhang, F. and He, H. (2019) “TEDMPU: A Tax Evasion Detection Method Based on Positive and Unlabeled Learning”, 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, pp. 1681-1686. D3
Xiangyu, X., Youlin, Y. and Qicheng, X. (2018) “Intelligent Identification of Corporate Tax Evasion Based on LM Neural Network”, 2018 37th Chinese Control Conference (CCC), Wuhan, pp. 4507-4511. D3
Ying, Q., Xiaoxin, H. and Weige, J. (2019). “Research on Tax Inspection Case Selection Model Based on Bayesian Network”. In: Proceedings of the 2019 2nd International Conference on Information Management and Management Sciences (IMMS 2019), Chengdu, China, pp. 198-202. D3
Zhu, X., Yan, Z., Ruan, J., Zheng, Q., Dong, B. (2018) “IRTED-TL: An Inter-Region Tax Evasion Detection Method Based on Transfer Learning,” 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), New York, NY, pp. 1224-1235. D3
Baesens, B., Vlasselaer V. V. and Verbeke W. (2015) “Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection”. New Jersey: John Wiley & Sons. D3
de Roux, D.; Pérez, B.; Moreno, A.; Villamil, P. and Figueroa, C. (2018). “Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach”. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18), Londor, UK, pp. 2015-222. D3
Dias, M. and Becker, K. (2017). “Identificação de Candidatos à Fiscalização por Evasão do Tributo ISS”. In: 5th Symposium on Knowledge Discovery, Mining and Learning, Uberlândia, MG, 2017. D3
Didimo, W., Grilli, L., Liotta, G., Menconi, L., Montecchiani, F. and Pagliuca D. (2020) “Combining Network Visualization and Data Mining for Tax Risk Assessment”, in IEEE Access, vol. 8, pp. 16073-16086. D3
Dybå, T. and Dingsøyr, T. (2008). “Empirical studies of agile software development: A systematic review”. Information and Software Technology. vol. 50, pp. 833-859. D3
Goumagias, N., Hristu-Varsakelis, D., and Assael, Y. (2018). “Using Deep Q-learning to understand the tax evasion behavior of risk-averse firms”. Expert Systems with Applications, vol. 101, pp. 258-270. D3
Jupri, M. and Sarno R. (2018) “Taxpayer compliance classification using C4.5, SVM, KNN, Naive Bayes and MLP”. 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, pp. 297-303. D3
Kitchenham, B. and Charters, S. (2007) “Guidelines for performing systematic literature reviews in software engineering”. D3
Kleanthous, C., and Chatzis, S.P. (2019). “Gated Mixture Variational Autoencoders for Value Added Tax audit case selection”. Knowledge-Based Systems, vol. 188, 105048. D3
Lismont, J., Cardinaels, E., Bruynseels, L., Groote, S.D., Baesens, B., Lemahieu, W., and Vanthienen, J. (2018). “Predicting tax avoidance by means of social network analytics”. Decision Support Systems, vol. 108, pp. 13-24. D3
Mathews, J., Mehta, P., Kuchibhotla, S., Bisht, D., S. Chintapalli , B. and Rao S. V. K. V.(2018) “Regression Analysis towards Estimating Tax Evasion in Goods and Services Tax”, 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Santiago, pp. 758-761. D3
Matos, T., Macedo, J. A., Lettich, F., Monteiro, J. M., Renso, C., Perego and R., Nardini, F. M. (2019). “Leveraging Feature Selection To Detect Potential Tax Fraudsters”. Expert Systems with Applications. vol. 145, 113128. D3
Matos, T.; Macedo, J. A. and Monteiro, J. M. (2015) “An Empirical Method for Discovering Tax Fraudsters”. In: IDEAS '15: Proceedings of the 19th International Database Engineering & Applications Symposium, Yokohama, Japão, p. 41-48. D3
Mehta, P., Mathews, J., Rao, S. V. K.V., Kumar, K. S., Suryamukhi , K., Babu C. S. (2019) “Identifying Malicious Dealers in Goods and Services Tax”, 2019 IEEE 4thInternational Conference on Big Data Analytics (ICBDA), Suzhou, China, pp. 312-316. D3
Mehta, P., Mathews, J., Suryamukhi, K., Kumar, K. S. and Babu, C. S. (2018) “Predictive Modeling for Identifying Return Defaulters in Goods and Services Tax”, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, pp. 631-637. D3
Okoli, C., Schabram, K. (2010) “A Guide to Conducting a Systematic Literature Review of Information Systems Research,” . Sprouts: Working Papers on Information Systems, 10(26). D3
Ouzzani M., Hammady H., Fedorowicz Z. and Elmagarmid A. (2016) “Rayyan-a Web and Mobile App for Systematic Reviews”. Syst Rev. 2016; 5(1): 210. D3
Ying, Q., Xiaoxin, H. and Weige, J. (2019). “Research on Tax Inspection Case Selection Model Based on Bayesian Network”. In Proceedings of the 2019 2nd International Conference on Information Management and Management Sciences (IMMS 2019). Association for Computing Machinery, New York, NY, USA, pp. 198–202. D3
Rahimikia, E., Mohammadi, S., Rahmani, T. and Ghazanfari, M. (2017). “Detecting corporate tax evasion using a hybrid intelligent system: A case study of Iran”. International Journal of Accounting Information Systems. Vol. 25, pp. 1-17. D3
Ruan, J., Yan, Z., Dong, B., Zheng, Q., Qian, B. (2018). “Identifying Suspicious Groups of Affiliated-Transaction-based Tax Evasion in Big Data”. Information Sciences, vol. 477, pp. 508-532. D3
Vanhoeyveld, J., Martens, D., Peeters, B. (2020). “Value-added tax fraud detection with scalable anomaly detection techniques”. Applied Soft Computing, vol. 86, 105895. D3
Wei, R., Dong, B., Zheng, Q., Zhu, X., Ruan, J. and He, H. (2019). “Unsupervised Conditional Adversarial Networks for Tax Evasion Detection”. 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, pp. 1675-1680. D3
Wu, Y., Zheng, Q., Gao, Y., Dong, B., Wei, R., Zhang, F. and He, H. (2019) “TEDMPU: A Tax Evasion Detection Method Based on Positive and Unlabeled Learning”, 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, pp. 1681-1686. D3
Xiangyu, X., Youlin, Y. and Qicheng, X. (2018) “Intelligent Identification of Corporate Tax Evasion Based on LM Neural Network”, 2018 37th Chinese Control Conference (CCC), Wuhan, pp. 4507-4511. D3
Ying, Q., Xiaoxin, H. and Weige, J. (2019). “Research on Tax Inspection Case Selection Model Based on Bayesian Network”. In: Proceedings of the 2019 2nd International Conference on Information Management and Management Sciences (IMMS 2019), Chengdu, China, pp. 198-202. D3
Zhu, X., Yan, Z., Ruan, J., Zheng, Q., Dong, B. (2018) “IRTED-TL: An Inter-Region Tax Evasion Detection Method Based on Transfer Learning,” 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), New York, NY, pp. 1224-1235. D3
Publicado
30/06/2020
Como Citar
SOARES, Glauco de Vasconcelos; CUNHA, Rodrigo C. L. V; DE MEDEIROS FILHO, Fernando Erico .
O Uso de Inteligência Artificial no Combate à Evasão Fiscal: Uma Revisão Sistemática da Literatura. In: WORKSHOP DE COMPUTAÇÃO APLICADA EM GOVERNO ELETRÔNICO (WCGE), 8. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 60-71.
ISSN 2763-8723.
DOI: https://doi.org/10.5753/wcge.2020.11258.
