References
- Aivaz, K.A., Munteanu, I.F., & Chiriac A. (2022). An exploratory analysis of the dynamics of the activity of the Fiscal Antifraud Directorate General in the 2014-2020 period at the level of Romania. Technium Social Sciences Journal, 30, pp. 337-347
- Alm, J., & Martinez-Vazquez, J. (2007). Tax Morale and Tax Evasion in Latin America, Andrew Young School of Policy Studies, Georgia University, International Studies Program, W.P. 07-32
- Bertotti, M.L., & Modanese, G. (2014a). Mathematical models for socio-economic problems. Springer INDAM Series, 6, pp. 124-134
- Bertotti, M.L., & Modanese, G. (2014b). Micro-to-macro models for income distribution in the absence and in the presence of tax evasion. Applied Mathematics Computation, 244, pp. 836-846
- Brasoveanu, I.V. (2010). Underground Economy and Corruption: the Major Problems of the Romanian Economy. Theoretical and Applied Economics, 11(552), pp. 91-102
- Brasoveanu, L.O., & Brasoveanu, I. (2008). The Correlation between Fiscal Policy and Economic Growth. Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, 7(524), pp. 19-26
- Brondolo, J. (2009). Collecting Taxes During an Economic Crisis: Challenges and Policy Options. IMF staff position note, SPN/09/17, pp.1-37
- Brooks, C. (2008). Introductory econometrics for finance. Cambridge University Press, UK, Fourth Edition
- Chandola, V., Banerjee, A., & Kumar, V., (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), pp. 1-58
- Chiriac, A., Nisulescu, I., & Aivaz, K.A. (2021). Fraud - A multidisciplinary element. Famous case studies in such different fields. Technium Social Sciences Journal, 26(1), pp. 930-943
- Fisman, R., & Shang-Jin, W. (2004). Tax Rates and Tax Evasion: Evidence from ‘Missing Imports’ in China. Journal of Political Economy, 112 (2), pp. 471-496
- Gupta, R. (2005). Asymmetric Information, Tax Evasion and Alternative Instruments of Government Revenue. Economics Working Papers, 200533
- Hilal, W., Gadsden, S.A., & Yawney, J. (2022). Financial Fraud: A review of Anomaly Detection Techniques and recent Advances. Expert systems With applications, 193, 116429
- Ngai, E., Hu, Y., Wong, Y., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), pp. 559-569
- Stan, M.I., Rus, M., & Tasente, T. (2020). Young people’s perception of the measures taken by the authorities in the context of the Covid-19 pandemic. Technium Social Sciences Journal, 7(1), pp. 18-27
- West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: A comprehensive review. Computers & Security, 57, pp. 47-66