Ahmed, S. A. (2017). Practical application of anti-money laundering requirements in Bangladesh: an insight into the disparity between anti-money laundering methods and their effectiveness based on resources and infrastructure. Journal of Money Laundering
Alsuwailem, A. A. S., & Saudagar, A. K. J. (2020). Anti-money laundering systems: a systematic literature review. Journal of Money Laundering Control, 23(4), 833-848.
https://doi.org/10.1108/jmlc-02-2020-0018
Barone, R., & Schneider, F. (2018). Money Laundering: A Review Essay and Policy Implication: SSRN.
Bhattacharya, D. (2018). Bangladesh’s Graduation from the Least Developed Countries Group: Pitfalls and Promises: Routledge.
Bozhilova, K. (2018). European Union: European commission proposes new anti-money laundering directives.
Dreżewski, R., Sepielak, J., & Filipkowski, W. (2015). The application of social network analysis algorithms in a system supporting money laundering detection. Information Sciences, 295, 18-32.
https://doi.org/10.1016/j.ins.2014.10.015
Ferwerda, J., Kattenberg, M., Chang, H.-H., Unger, B., Groot, L., & Bikker, J. A. (2013). Gravity models of trade-based money laundering. Applied economics, 45(22), 3170-3182.
https://doi.org/10.1080/00036846.2012.699190
Getoor, L., Friedman, N., Koller, D., & Taskar, B. (2001). Learning probabilistic models of relational structure. Paper presented at the ICML.
Ghulam, Y., & Szalay, B. (2023). Investigating the determinants of money laundering risk. Journal of Money Laundering Control.
Haq, M. Z., Ayub, Z. A., Yusoff, Z. M., & Khan, M. A. A. (2021). Factors influencing anti-money laundering regulatory approaches towards casinos and cryptocurrencies in Bangladesh. Journal of Money Laundering Control, 25(2), 445-454.
Hassan, M., & Schneider, F. (2016). Size and development of the shadow economies of 157 countries worldwide: Updated and new measures from 1999 to 2013. SSRN Electronic Journal.
https://doi.org/10.2139/ssrn.2861026
Hossen, M. S., Roy, S., & Masum, M. H. (2023). Financial performance of engineering companies in an emerging economy: Before and after COVID-19. Research In Management and Accounting (RIMA), 6(2), 73-90.
https://doi.org/10.33508/rima.v6i2.4943
Integrity, G. F. (2019). Illicit financial flows to and from 148 developing countries: 2006-2015. Washington, DC: Global Financial Integrity.
Jullum, M., Løland, A., Huseby, R. B., et al. (2020). Detecting money laundering transactions with machine learning. Journal of Money Laundering Control, 23(1), 173-186.
https://doi.org/10.1108/jmlc-07-2019-0055
Kaufmann, D., & Kaliberda, A. (1996). Integrating the unofficial economy into the dynamics of post-socialist economies: A framework of analysis and evidence. The International Politics of Eurasia: v. 8: Economic Transition in Russia and the New States of
Kumar, S., Ahmed, R., Bharany, S., et al. (2022). Exploitation of Machine Learning Algorithms for Detecting Financial Crimes Based on Customers’ Behavior. Sustainability, 14(21), 13875.
https://doi.org/10.3390/su142113875
Masum, M. H., Latiff, A. R. A., & Osman, M. N. H. (2020). Voluntary Reporting, Sustainable Reporting and Transition Economy. International Business and Accounting Research Journal, 4(2), 81-88.
https://doi.org/10.15294/ibarj.v4i2.126
Masum, M. H., Latiff, A. R. A., & Osman, M. N. H. (2021). Determinants of corporate voluntary disclosure in a transition economy. Problems and Perspectives in Management, 18(4), 130.
Masum, M. H., Latiff, A. R. A., Osman, M. N. H., et al. (2023). Impact of corporate characteristics on voluntary disclosure: A Managerial insight. Polish Journal of Management Studies, 28(1).
https://doi.org/10.17512/pjms.2023.28.1.12
Medina, L., & Schneider, F. G. (2019). Shedding Light on the Shadow Economy: A Global Database and the Interaction with the Official One. SSRN Electronic Journal.
https://doi.org/10.2139/ssrn.3502028
Mubalaike, A. M., & Adali, E. (2018). Deep learning approach for intelligent financial fraud detection system. Paper presented at the 2018 3rd International Conference on Computer Science and Engineering (UBMK).
https://doi.org/10.1109/ubmk.2018.8566574
Rahman, S. H., Ahmed, K. N., Aziz, S. S., et al. (2015). The State of Governance Bangladesh 2014-2015: institutions, outcomes, accountability.
Sivaguru, D., & Tilakasiri, K. (2023). The phenomenon of trade-based money laundering (TBML)—a critical review in Sri Lankan context. Journal of Money Laundering Control, 26(6), 1088–1099.
https://doi.org/10.1108/jmlc-10-2022-0151
Solaiman, S. (2018). “Captured by evils”—combating black money, corruption and money laundering in Bangladesh: The dog must bark to keep predators away. Journal of Money Laundering Control, 21(3), 264-289.
https://doi.org/10.1108/jmlc-06-2017-0020
Suresh, C., Reddy, K. T., & Sweta, N. (2016). A hybrid approach for detecting suspicious accounts in money laundering using data mining techniques. International Journal of Information Technology and Computer Science (IJITCS), 8(5), 37.
https://doi.org/10
Syed Mustapha Nazri, S. N. F., Zolkaflil, S., & Omar, N. (2019). Mitigating financial leakages through effective money laundering investigation. Managerial Auditing Journal, 34(2), 189-207.
https://doi.org/10.1108/maj-03-2018-1830
Tiwari, M., Gepp, A., & Kumar, K. (2020). A review of money laundering literature: the state of research in key areas. Pacific Accounting Review, 32(2), 271–303.
https://doi.org/10.1108/par-06-2019-0065
Waris, A., & Khan, L. L. (2013). Black money whitening law: a study from Bangladesh.
Zafarullah, H., & Haque, H. (2023). Policies, instrumentalities, compliance and control: combatting money laundering in Bangladesh. Journal of Money Laundering Control, 26(1), 189-204.
https://doi.org/10.1108/jmlc-10-2021-0109
Zhang, Z., Salerno, J. J., & Yu, P. S. (2003). Applying data mining in investigating money laundering crimes. Paper presented at the Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining.
https://doi.org/10.11
