A systematic literature review of illicit financial flows and money laundering: Current state of research and estimation methods
By: Anna Popik-Mazur
References
- Achim, M. V., Borlea, S. N., Vǎidean, V. L., Rus, A. I., & Dobre, F. (2024). The impact of intelligence on economic and financial crime: A cross-country study. Singapore Economic Review, 69(8), 2521-2554. https://doi.org/10.1142/S0217590820500782
- Ajide, F. M., & Ojeyinka, T. A. (2024). Benefit or burden? An exploratory analysis of the impact of anti-money laundering regulations on sustainable development in developing economies. Sustainable Development, 32(3), 2417-2434. https://doi.org/10.1002/sd.2789
- Akartuna, E., Johnson, S., & Thornton, A. (2024). A holistic network analysis of the money laundering threat landscape: Assessing criminal typologies, resilience and implications for disruption. Journal of Quantitative Criminology, 41, 173-214. https://doi.org/10.1007/s10940-024-09594-3
- Alarab, I., & Prakoonwit, S. (2024). Robust recurrent graph convolutional network approach based sequential prediction of illicit transactions in cryptocurrencies. Multimedia Tools and Applications, 83(20), 58449-58464. https://doi.org/10.1007/s11042-023-17323-4
- Alarab, I., Prakoonwit, S., & Nacer, M. I. (2020). Comparative analysis using supervised learning methods for anti-money laundering in bitcoin. In Proceedings of the 2020 5th International Conference on Machine Learning Technologies, Virtual, 19-21 June 2020 (pp. 11-17). https://doi.org/10.1145/3409073.3409078
- Alexandre, C. R., & Balsa, J. (2023). Incorporating machine learning and a risk-based strategy in an anti-money laundering multiagent system. Expert Systems with Applications, 217, 119500. https://doi.org/10.1016/j.eswa.2023.119500
- Aljassmi, M., Gamal, A. A. M., Abdul Jalil, N., David, J., & Viswanathan, K. K. (2024). Estimating the magnitude of money laundering in the United Arab Emirates (UAE): Evidence from the currency demand approach (CDA). Journal of Money Laundering Control, 27(2), 332-347. https://doi.org/10.1108/JMLC-02-2023-0043
- Al-Kasawnih, A. (2019). How important is the lesson of the correlation between advancing the globalization process and intensifying the money laundering phenomenon? Proceedings of the 2020 5th International Conference on Machine Learning Technologies, 13(1), 1267-1276. https://doi.org/10.2478/picbe-2019-0112
- Alkhatib, K., & Abualigah, S. (2023). Anti-laundering approach for Bitcoin transactions. In 2023 14th International Conference on Information and Communication Systems, Irbid, Jordan, 2023 (pp. 1-6). IEEE. https://doi.org/10.1109/ICICS60529.2023.10330498
- Al-Tawil, T. N. (2023). Anti-money laundering regulation of cryptocurrency: UAE and global approaches. Journal of Money Laundering Control, 26(6), 1150-1164. https://doi.org/10.1108/JMLC-07-2022-0109
- Altman, E., Blanuša, J., von Niederhäusern, L., Egressy, B., Anghel, A., & Atasu, K. (2023). Realistic synthetic financial transactions for anti-money laundering models. In 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks. Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2306.16424
- Amiram, D., Jorgensen, B. N., & Rabetti, D. (2022). Coins for bombs: The predictive ability of on-chain transfers for terrorist attacks. Journal of Accounting Research, 60(2), 427-466. https://doi.org/10.1111/1475-679X.12430
- Amjad, R. M., Rafay, A., Arshed, N., Munir, M., & Amjad, M. M. (2022). Non-linear impact of globalization on financial crimes: A case of developing economies. Journal of Money Laundering Control, 25(2), 358-375. https://doi.org/10.1108/JMLC-03-2021-0023
- Anagnostou, M., & Doberstein, B. (2022). Illegal wildlife trade and other organised crime: A scoping review. Ambio, 51(7), 1615-1631. https://doi.org/10.1007/s13280-021-01675-y
- Atems, B., & Mullen, J. K. (2016). Outward FDI from the USA and host country financial transparency. Journal of International Trade & Economic Development, 25(8), 8. https://doi.org/10.1080/09638199.2016.1191526
- Babatunde, M. A., & Afolabi, J. A. (2023). Growth effect of trade misinvoicing in Sub-Saharan Africa: The role of governance. International Journal of Development Issues, 22(2), 241-254. https://doi.org/10.1108/IJDI-01-2023-0004
- Badawi, E., & Jourdan, G.-V. (2020). Cryptocurrencies emerging threats and defensive mechanisms: A systematic literature review. IEEE Access, 8, 200021-200037. https://doi.org/10.1109/ACCESS.2020.3034816
- Baek, H., Oh, J., Kim, C. Y., & Lee, K. (2019). A model for detecting cryptocurrency transactions with discernible purpose. In 2019 11th International Conference on Ubiquitous and Future Networks (ICUFN), 2019-July (pp. 713-717). IEEE. https://doi.org/10.1109/ICUFN.2019.8806126
- Barone, R. (2023). Home sweet home, how money laundering pollutes the real estate market: An agent based model. Journal of Economic Interaction and Coordination, 18, 779-806. https://doi.org/10.1007/s11403-023-00391-y
- Barone, R., Masciandaro, D., & Schneider, F. (2022). Corruption and money laundering: You scratch my back, I’ll scratch yours. Metroeconomica, 73(1), 318-342. https://doi.org/10.1111/meca.12365
- Barro, M., & Rausch, C. (2024). Toward trust and transparency in the Netherlands: Art market compliance (and defiance) viewed through financial transactions monitoring at a major Dutch bank. In T.Ø. Kuldova, J. Østbø, C. Shore (Eds.), Compliance, defiance, and ‘dirty’ luxury: New perspectives on anti-corruption in elite contexts (pp. 237-264). Springer Nature. https://doi.org/10.1007/978-3-031-57140-4_8
- Biagioli, A. (2008). Financial crime as a threat to the wealth of nations: A cost-effectiveness approach. Journal of Money Laundering Control, 11(1), 88-95. https://doi.org/10.1108/13685200810844523
- Bojnec, Š., & Fertő, I. (2018). Globalization and outward foreign direct investment. Emerging Markets Finance and Trade, 54(1), 88-99. https://doi.org/10.1080/1540496X.2016.1234372
- Brandt, K. (2023). Illicit financial flows and developing countries: A review of methods and evidence. Journal of Economic Surveys, 37(3), 789-820. https://doi.org/10.1111/joes.12518
- Camdessus, M. (1998). Money laundering: The importance of international countermeasures. IMF. https://www.imf.org/en/News/Articles/2015/09/28/04/53/sp021098
- Cassara, J. A., & Poncy, C. (2015). Trade-based money laundering: The next frontier in international money laundering enforcement. Wiley. https://doi.org/10.1002/9781119125389
- Chainalysis. (2024, July 11). The Blockchain Data Platform. https://www.chainalysis.com/
- Cheng, D., Ye, Y., Xiang, S., Ma, Z., Zhang, Y., & Jiang, C. (2023). Anti-money laundering by group-aware deep graph learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12444-12457. https://doi.org/10.1109/TKDE.2023.3272396
- Cindori, S., & Slovic, J. (2017). Identifying money laundering in business operations as a factor for estimating risk. International Journal of Innovation and Economic Development, 3(3), 7-16. https://doi.org/10.18775/ijied.1849-7551-7020.2015.33.2001
- Clavijo Suntura, J. H. (2022). Improving the detection and reporting of suspicious financial transactions: The Spanish case. Journal of Money Laundering Control, 25(4), 882-892. https://doi.org/10.1108/JMLC-09-2021-0095
- Collin, M. (2020). Illicit financial flows: Concepts, measurement, and evidence. World Bank Research Observer, 35(1), 44-86. https://doi.org/10.1093/wbro/lkz007
- Cretu-Adatte, C., Zbinden, R., Brunoni, L., Bunning, H., Azi, J. W., & Beaudet-Labrecque, O. (2024). How do Ivorian cyberfraudsters manage their criminal proceeds? European Journal on Criminal Policy and Research, 30(3), 359-378. https://doi.org/10.1007/s10610-024-09597-7
- Crystal. (2024, July 11). Crystal Intelligence Analytics for Crypto Compliance. https://crystalintelligence.com/
- Cunha, L. L., & Brito, M. A. (2023). Anomaly detection in cryptocurrency transactions with active learning. In P. Kommers, M. Macedo, G. C. Peng, A. Abraham, & L. Rodrigues (Eds.), Proceedings of the International Conferences on ICT, Society, and Human Beings 2023, ICT 2023; and e-Health 2023, EH 2023; Connected Smart Cities 2023, CSC 2023; and Big Data Analytics, Data Mining and Computational Intelligence 2023, BigDaCI 2023 (pp. 359-363). IADIS Press.
- Dileo, I., & Giorno, S. D. (2023). The impact of extra liquidity on firm-level employment. An analysis from an Italian case study. Industria, 44(4), 677-699. https://www.rivisteweb.it/doi/10.1430/112975
- van Duyne, P. C., & Soudijn, M. R. J. (2009). Hot money, hot stones and hot air: Crime-money threat, real estate and real concern. Journal of Money Laundering Control, 12(2), 173-188. https://doi.org/10.1108/13685200910951929
- van Eck, N. J., & Waltman, L. (2011). Text mining and visualization using VOSviewer. arXiv. https://doi.org/10.48550/arXiv.1109.2058
- van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact (pp. 285-320). Springer. https://doi.org/10.1007/978-3-319-10377-8_13
- Elmougy, Y., & Liu, L. (2023). Demystifying fraudulent transactions and illicit nodes in the bitcoin network for financial forensics. In KDD’23: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 3979-3990). ACM SIGKDD. https://doi.org/10.1145/3580305.3599803
- Farooqi, M. N. (2010). Curbing the use of Hawala for money laundering and terrorist financing: Global regulatory response and future challenges. International Journal of Business Governance and Ethics, 5(1-2), 64-75. https://doi.org/10.1504/IJBGE.2010.029556
- Farrugia, S., Ellul, J., & Azzopardi, G. (2020). Detection of illicit accounts over the Ethereum blockchain. Expert Systems with Applications, 150, 113318. https://doi.org/10.1016/j.eswa.2020.113318
- FATF. (2021). Opportunities and challenges of new technologies for AML/CFT. Financial Action Task Force. https://www.fatf-gafi.org/en/publications/Digitaltransformation/Opportunities-challenges-new-technologies-for-aml-cft.html
- Feridun, M. (2024). Cross-jurisdictional financial crime risks: What can we learn from the UK regulatory data? Journal of Financial Crime, 31(3), 608-617. https://doi.org/10.1108/JFC-03-2023-0044
- Ferretti, S., D’Angelo, G., & Ghini, V. (2024). On the use of heterogeneous graph neural networks for detecting malicious activities: A case study with cryptocurrencies. In OASIS’24 – Proceedings of the Workshop on Open Challenges in Online Social Media, Held in Conjunction with ACM Conference on Hypertext and Social Media (pp. 33-40). https://doi.org/10.1145/3677117.3685009
- 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
- Ferwerda, J., & Kleemans, E. R. (2019). Estimating money laundering risks: An application to business sectors in the Netherlands. European Journal on Criminal Policy and Research, 25, 45-62. https://doi.org/10.1007/s10610-018-9391-4
- Ferwerda, J., & Unger, B. (2021). How big are illicit financial flows? The hot phase of IFF estimations. In B. Unger, L. Rossel Flores, & J. Ferwerda (Eds.), Combating fiscal fraud and empowering regulators: Bringing tax money back into the COFFERS (pp. 75-88). Oxford University Press. https://doi.org/10.1093/oso/9780198854722.003.0005
- Ferwerda, J., van Saase, A., Unger, B., & Getzner, M. (2020). Estimating money laundering flows with a gravity model-based simulation. Scientific Reports, 10(1), 18552. https://doi.org/10.1038/s41598-020-75653-x
- Gara, M., Giammatteo, M., & Tosti, E. (2019). Magic mirror in my hand horizontal ellipsis. How trade mirror statistics can help us detect illegal financial flows. The World Economy, 42(11), 3120-3147. https://doi.org/10.1111/twec.12840
- Ghalwash, A. Z., El Khameesy, N., Magdi, D. A., & Joshi, A. (Eds.). (2020). Internet of things – applications and future: Proceedings of ITAF 2019 (Vol. 114). Springer Singapore. https://doi.org/10.1007/978-981-15-3075-3
- Gilmore, W. C. (2004). Dirty money: The evolution of international measures to counter money laundering and the financing of terrorism (3rd ed., Vol. 559). Council of Europe Publishing.
- Gilmour, N. (2023). Crime scripting the criminal activities of money laundering – holistically. Journal of Money Laundering Control, 26(3), 594-608. https://doi.org/10.1108/JMLC-09-2020-0109
- Goecks, L. S., Korzenowski, A. L., Terra Neto, P. G., de Souza, D. L., & Mareth, T. (2022). Anti-money laundering and financial fraud detection: A systematic literature review. Intelligent Systems in Accounting, Finance and Management, 29(2), 71-85. https://doi.org/10.1002/isaf.1509
- Goldbarsht, D. (2024). Adapting confiscation and anti-money laundering laws to the digital economy: Exploring the Australian interplay between proceeds and technology. Journal of Money Laundering Control, 27(3), 472-488. https://doi.org/10.1108/JMLC-09-2023-0142
- Guo, C., Zhang, S., Zhang, P., Alkubati, M., & Song, J. (2023). LB-GLAT: Long-term bi-graph layer attention convolutional network for anti-money laundering in transactional blockchain. Mathematics, 11(18), 3927. https://doi.org/10.3390/math11183927
- Hacken. (2024, December 24). The Hacken 2024 Web3 security report. https://hacken.io/insights/2024-security-report/
- von Haenisch, H.-J., & Egner, T. (2024). Know your customer: Unravelling the challenges. Journal of Payments Strategy and Systems, 18(1), 30-38. https://doi.org/10.69554/KLRO4841
- Hanif, N., Javaid, A., Arshed, N., & Rafay, A. (2023). Foreign exploration of immature markets and incidence of money laundering. Journal of Money Laundering Control, 26(6), 1117-1130. https://doi.org/10.1108/JMLC-10-2022-0153
- Hendriyetty, N., & Grewal, B. S. (2017). Macroeconomics of money laundering: Effects and measurements. Journal of Financial Crime, 24(1), 65-81. https://doi.org/10.1108/JFC-01-2016-0004
- Hofmann, R., & Lustenberger, L. (2023). Reporting obligations for attorneys in money laundering cases: Attorney-client privilege under pressure? German Law Journal, 24(5), 825-837. https://doi.org/10.1017/glj.2023.50
- Huang, Z., Huang, Y., Qian, P., Chen, J., & He, Q. (2023). Demystifying bitcoin address behavior via graph neural networks. In Proceedings of the International Conference on Data Engineering (ICDE 2023), 2023-April (pp. 1747-1760). IEEE. https://doi.org/10.1109/ICDE55515.2023.00137
- Irwin, A. S. M., Choo, K.-K. R., & Liu, L. (2012). Modelling of money laundering and terrorism financing typologies. Journal of Money Laundering Control, 15(3), 316-335. https://doi.org/10.1108/13685201211238061
- Islam, M. A., & Nasir, M. K. (2020). Evaluation of money laundering risk of bank accounts using naive Bayes classification. Journal of Engineering Science and Technology, 15(5), 3481-3493. https://jestec.taylors.edu.my/Vol%2015%20issue%205%20October%202020/15_5_43.pdf
- Isolauri, E. A., & Ameer, I. (2023). Money laundering as a transnational business phenomenon: A systematic review and future agenda. Critical Perspectives on International Business, 19(3), 426-468. https://doi.org/10.1108/cpoib-10-2021-0088
- Javaid, A., & Arshed, N. (2022). Demand for money laundering in developing countries and its deterrence: A quantitative analysis. Journal of Money Laundering Control, 25(3), 625-636. https://doi.org/10.1108/JMLC-06-2021-0063
- Jensen, R. I. T., & Iosifidis, A. (2023a). Fighting money laundering with statistics and machine learning. IEEE Access, 11, 8889-8903. https://doi.org/10.1109/ACCESS.2023.3239549
- Jensen, R. I. T., & Iosifidis, A. (2023b). Qualifying and raising anti-money laundering alarms with deep learning. Expert Systems with Applications, 214, 119037. https://doi.org/10.1016/j.eswa.2022.119037
- Jullum, M., Løland, A., Huseby, R. B., Ånonsen, G., & Lorentzen, J. (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
- Kalokoh, A. (2024). Money laundering and terrorist financing risks and democratic governance: A global correlational analysis. Journal of Money Laundering Control, 27(5), 825-844. https://doi.org/10.1108/JMLC-09-2023-0151
- Khan, W., Jawaid, S. T., & Arif, I. (2018). Where does a nation’s wealth go? Evidence from a third world country. Journal of Money Laundering Control, 21(3), 426-476. https://doi.org/10.1108/JMLC-01-2017-0005
- Koelbing, M., Kieseberg, K., Çulha, C., Garn, B., & Simos, D. E. (2024). Modelling smurfing patterns in cryptocurrencies with integer partitions. IET Blockchain, 4(S1), 659-680. https://doi.org/10.1049/blc2.12087
- de Koker, L., Howell, J., & Morris, N. (2023). Economic consequences of greylisting by the Financial Action Task Force. Risks, 11(5). https://doi.org/10.3390/risks11050081
- Koo, K., Park, M., & Yoon, B. (2024). A suspicious financial transaction detection model using autoencoder and risk-based approach. IEEE ACCESS, 12, 68926-68939. https://doi.org/10.1109/ACCESS.2024.3399824
- Korejo, M. S., Rajamanickam, R., & Md Said, M. H. (2021). The concept of money laundering: A quest for legal definition. Journal of Money Laundering Control, 24(4), 725-736. https://doi.org/10.1108/JMLC-05-2020-0045
- Krauskopf, E. (2018). A bibiliometric analysis of the Journal of Infection and Public Health: 2008-2016. Journal of Infection and Public Health, 11(2), 224-229. https://doi.org/10.1016/j.jiph.2017.12.011
- Krishnan, L. P., Vakilinia, I., Reddivari, S., & Ahuja, S. (2023). Scams and solutions in cryptocurrencies – a survey analyzing existing machine learning models. Information, 14(3), 171. https://doi.org/10.3390/info14030171
- Kushelevitch, M. (2024). Unveiling the crypto-terror nexus: Law enforcement intelligence challenges against terrorist financing. Journal of Financial Crime, 32(2), 403-415. https://doi.org/10.1108/JFC-12-2023-0330
- Kute, D. V., Pradhan, B., Shukla, N., & Alamri, A. (2021). Deep learning and explainable artificial intelligence techniques applied for detecting money laundering – a critical review. IEEE Access, 9, 82300-82317. https://doi.org/10.1109/ACCESS.2021.3086230
- Labanca, D., Primerano, L., Markland-Montgomery, M., Polino, M., Carminati, M., & Zanero, S. (2022). Amaretto: An active learning framework for money laundering detection. IEEE Access, 10, 41720-41739. https://doi.org/10.1109/ACCESS.2022.3167699
- Lazarus, S. (2025). Cybercriminal networks and operational dynamics of Business Email Compromise (BEC) scammers: Insights from the “Black Axe” confraternity. Deviant Behavior, 46(4), 456-480. https://doi.org/10.1080/01639625.2024.2352049
- Levchenko, V., Boyko, A., Bozhenko, V., & Mynenko, S. (2019). Money laundering risk in developing and transitive economies: Analysis of cyclic component of time series. Business: Theory and Practice, 20, 492-508. https://doi.org/10.3846/btp.2019.46
- Levi, M. (2020). Evaluating the control of money laundering and its underlying offences: The search for meaningful data. Asian Journal of Criminology, 15(4), 4, 301-320. https://doi.org/10.1007/s11417-020-09319-y
- Li, L., Verbeek, H., Chen, H., Loukides, G., Gwadera, R., Stougie, L., & Pissis, S. P. (2024). Heavy nodes in a small neighborhood: Exact and peeling algorithms with applications. IEEE Transactions on Knowledge and Data Engineering, 37(4), 1853-1870. https://doi.org/10.1109/TKDE.2024.3515875
- Lillo, F., Garcia, L., & Santander, V. (2017). Dynamics of global remittances: A graph-based analysis. Mathematical Social Sciences, 87, 64-71. https://doi.org/10.1016/j.mathsocsci.2017.02.005
- Llaque Sánchez, F. R., Tarazona, C. V., Ruffner, J. R., & Llaque Sánchez, A. H. (2023). Tax rules on wealth imbalance and the investigation of money laundering in Peru. Revista Criminalidad, 65(2), 159-170. http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S1794-31082023000200159&lng=en&nrm=iso&tlng=en
- Lo, W. W., Kulatilleke, G. K., Sarhan, M., Layeghy, S., & Portmann, M. (2023). Inspection- L: Self-supervised GNN node embeddings for money laundering detection in bitcoin. Applied Intelligence, 53(16), 19406-19417. https://doi.org/10.1007/s10489-023-04504-9
- Loayza, N., Villa, E., & Misas, M. (2019). Illicit activity and money laundering from an economic growth perspective: A model and an application to Colombia. Journal of Economic Behavior & Organization, 159, 442-487. https://doi.org/10.1016/j.jebo.2017.10.002
- Lokanan, M. E. (2022). Predicting money laundering using machine learning and artificial neural networks algorithms in banks. Journal of Applied Security Research, 17(4), 524-542. https://doi.org/10.1080/19361610.2022.2114744
- Lorenz, J., Silva, M. I., Aparício, D., Ascensão, J. T., & Bizarro, P. (2020). Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity. In Proceedings of the First ACM International Conference on AI in Finance (ICAIF ’20) (Article 23). Association for Computing Machinery. https://doi.org/10.1145/3383455.3422549
- Lucas, P. J., Baird, J., Arai, L., Law, C., & Roberts, H. M. (2007). Worked examples of alternative methods for the synthesis of qualitative and quantitative research in systematic reviews. BMC Medical Research Methodology, 7(1), 4. https://doi.org/10.1186/1471-2288-7-4
- Lupton, C. (2023). Illegal wildlife trade: The critical role of the banking sector in combating money laundering. Journal of Money Laundering Control, 26(7), 181-196. https://doi.org/10.1108/JMLC-06-2023-0105
- Lyeonov, S., Draskovic, V., Kubaščikova, Z., & Fenyves, V. (2024). Artificial intelligence and machine learning in combating illegal financial operations: Bibliometric analysis. Human Technology, 20(2), 325-360. https://doi.org/10.14254/1795-6889.2024.20-2.5
- Marzouk, M. J. (2022). Trade-based money laundering (TBML) empowers criminals to run free post-Brexit. Journal of Money Laundering Control, 25(2), 376-387. https://doi.org/10.1108/JMLC-04-2021-0040
- Masrom, S., Tarmizi, M. A., Halid, S., Rahman, R. A., Abd Rahman, A. S., & Ibrahim, R. (2023). Machine learning in predicting anti-money laundering compliance with protection motivation theory among professional accountants. International Journal of Advanced and Applied Sciences, 10(7), 48-53. https://doi.org/10.21833/ijaas.2023.07.007
- Mbarek, S., Trabelsi, D., & Berne, M. (2019). Are virtual currencies virtuous? Ethical and environmental issues. In S. Goutte, K. Guesmi, S. Saadi (Eds.), Cryptofinance and mechanisms of exchange (pp. 29-50). Contributions to Management Science. Springer. https://doi.org/10.1007/978-3-030-30738-7_2
- Milon, M. N. U., & Zafarullah, H. (2024). Uncovering the depths of trade-based money laundering: Evidence from a seaport in Bangladesh. Journal of Money Laundering Control, 27(3), 604-618. https://doi.org/10.1108/JMLC-03-2023-0063
- Mniwasa, E. E. (2021). The anti-money laundering law in Tanzania: Whither the ongoing “war” against economic crimes? Journal of Money Laundering Control, 24(4), 869-907. https://doi.org/10.1108/JMLC-09-2020-0099
- Naheem, M. A. (2015). Trade based money laundering: Towards a working definition for the banking sector. Journal of Money Laundering Control, 18(4), 513-524. https://doi.org/10.1108/JMLC-01-2015-0002
- Nayyer, N., Javaid, N., Akbar, M., Aldegheishem, A., Alrajeh, N., & Jamil, M. (2023). A new framework for fraud detection in Bitcoin transactions through ensemble stacking model in smart cities. IEEE Access, 11, 90916-90938. https://doi.org/10.1109/ACCESS.2023.3308298
- Nazzari, M., & Riccardi, M. (2024). Cleaning mafia cash: An empirical analysis of the money laundering behaviour of 2800 Italian criminals. European Journal of Criminology, 21(4), 583-608. https://doi.org/10.1177/14773708231224981
- Di Nicola, A., & Terenghi, F. (2016). Managing finances in the illicit tobacco trade in Italy. Trends in Organized Crime, 19, 254-272. https://doi.org/10.1007/s12117-016-9274-3
- Oad, A., Razaque, A., Tolemyssov, A., Alotaibi, M., Alotaibi, B., & Zhao, C. (2021). Blockchain-enabled transaction scanning method for money laundering detection. Electronics, 10(15), 1766. https://doi.org/10.3390/electronics10151766
- OECD. (2014). Illicit financial flows from developing countries: Measuring OECD responses. https://doi.org/10.1787/9789264203501-en
- Olivier, E., & Nortje, W. (2024). Anti-money laundering and the ‘beneficial ownership’ amendments to South Africa’s Companies Act 71 of 2008. Potchefstroom Electronic Law Journal, 2. https://doi.org/10.17159/1727-3781/2024/v27i0a17752
- Otusanya, O. J., & Adeyeye, G. B. (2022). The dark side of tax havens in money laundering, capital flight and corruption in developing countries: Some evidence from Nigeria. Journal of Financial Crime, 29(1), 1. https://doi.org/10.1108/JFC-02-2021-0044
- Pataccini, L. (2023). The (un)usual suspects? Exploring the links between illicit financial flows, Russian money laundering and dependent financialization in the Baltic states. Competition and Change, 28(1), 3-22. https://doi.org/10.1177/10245294231177352
- Pellegrini, A. (2024). A research on the Italian fiscal shell companies turnover. Journal of Money Laundering Control, 27(6), 1092-1103. https://doi.org/10.1108/JMLC-03-2024-0055
- Perez, M. F., Brada, J. C., & Drabek, Z. (2012). Illicit money flows as motives for FDI. Journal of Comparative Economics, 40(1), 108-126. https://doi.org/10.1016/j.jce.2011.03.007
- Pettersson Ruiz, E., & Angelis, J. (2022). Combating money laundering with machine learning – applicability of supervised-learning algorithms at cryptocurrency exchanges. Journal of Money Laundering Control, 25(4), 766-778. https://doi.org/10.1108/JMLC-09-2021-0106
- Phetsouvanh, S., Oggier, F., & Datta, A. (2018). EGRET: Extortion graph exploration techniques in the Bitcoin network. In H. Tong, Z. Li, F. Zhu, & J. Yu (Eds.), Proceedings of the 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 244-251). IEEE. https://doi.org/10.1109/ICDMW.2018.00043
- Pocher, N., Zichichi, M., Merizzi, F., Shafiq, M. Z., & Ferretti, S. (2023). Detecting anomalous cryptocurrency transactions: An AML/CFT application of machine learning-based forensics. Electronic Markets, 33(1). https://doi.org/10.1007/s12525-023-00654-3
- Polanin, J. R., Pigott, T. D., Espelage, D. L., & Grotpeter, J. K. (2019). Best practice guidelines for abstract screening large-evidence systematic reviews and meta-analyses. Research Synthesis Methods, 10(3), 330-342. https://doi.org/10.1002/jrsm.1354
- Pontes, R., Lewis, N., McFarlane, P., & Craig, P. (2022). Anti-money laundering in the United Kingdom: New directions for a more effective regime. Journal of Money Laundering Control, 25(2), 401-413. https://doi.org/10.1108/JMLC-04-2021-0041
- Popik-Mazur, A. (2025). A systematic literature review of illicit financial flows and money laundering: Current state of research and estimation methods. RepOD. https://doi.org/10.18150/HQMTA7
- Prabowo, H. Y. (2024). Where is the life we have lost in living (beyond means)? An exploratory inquiry into the deceptive world of corruption and consumerism. Journal of Financial Crime, 31(6), 1540-1560. https://doi.org/10.1108/JFC-10-2023-0273
- Premti, A., Jafarinejad, M., & Balani, H. (2021). The impact of the Fourth Anti-Money Laundering Directive on the valuation of EU banks. Research in International Business and Finance, 57, 101397. https://doi.org/10.1016/j.ribaf.2021.101397
- Prendi, L., Borakaj, D., & Prendi, K. (2023). The new money laundering machine through cryptocurrency: Current and future public governance challenges. Corporate Law and Governance Review, 5(2), 84-91. https://doi.org/10.22495/clgrv5i2p9
- Reganati, F., & Oliva, M. (2018). Determinants of money laundering: Evidence from Italian regions. Journal of Money Laundering Control, 21(3), 402-413. https://doi.org/10.1108/JMLC-09-2017-0052
- Reite, E. J., Oust, A., Bang, R. M., & Maurstad, S. (2023). Changes in credit score, transaction volume, customer characteristics, and the probability of detecting suspicious transactions. Journal of Money Laundering Control, 26(6), 1165-1178. https://doi.org/10.1108/JMLC-06-2022-0087
- Reuter, P. (2013). Are estimates of the volume of money laundering either feasible or useful? In B. Unger & D. van der Linde (Eds.), Research handbook on money laundering (pp. 224-231). Edward Elgar Publishing. https://doi.org/10.4337/9780857934000.00028
- Reuter, P., & Riccardi, M. (2024). Introduction to special issue on “Understanding Money Laundering: Empirical and Theoretical Insights into Offenders, Typologies, and Determinants of Criminal Behaviour”. European Journal on Criminal Policy and Research, 30(3), 327-332. https://doi.org/10.1007/s10610-024-09604-x
- Riccardi, M., Milani, R., & Camerini, D. (2019). Assessing money laundering risk across regions. An application in Italy. European Journal on Criminal Policy and Research, 25(1), 21-43. https://doi.org/10.1007/s10610-018-9399-9
- Roman, J., Machuca, A., & Schaefer, T. (2022). Estimating the destination of Mexican-based laundered funds: An application of the modified Walker-Unger model. Journal of Money Laundering Control, 25(1), 224-248. https://doi.org/10.1108/JMLC-01-2021-0008
- Roman, J., & Schaefer, T. (2023). Developing a laundered funds destination theory: Applying the Walker–Unger gravity model to US-based money launderer country preference from 2000 to 2020. Journal of Money Laundering Control, 26(3), 640-664. https://doi.org/10.1108/JMLC-01-2022-0002
- Saenz, M., & Lewer, J. J. (2023). Estimates of trade based money laundering within the European Union. Applied Economics, 55(51), 5991-6003. https://doi.org/10.1080/00036846.2022.2141444
- Sartori, M., Seher, I., & Prasad, P. W. C. (2023). The illicit use of cryptocurrency on the darknet by cybercriminals to evade authorities. In S. C. Mukhopadhyay, S. M. N. A. Senanayake, & P. W. Ch. Withana (Eds.), Innovative Technologies in Intelligent Systems and Industrial Applications. CITISIA 2022 (Lecture Notes in Electrical Engineering, Vol. 1029; pp. 449-459). Springer Science and Business Media. https://doi.org/10.1007/978-3-031-29078-7_39
- Schneider, F. (2010). Turnover of organized crime and money laundering: Some preliminary empirical findings. Public Choice, 144(3), 473-486. https://doi.org/10.1007/s11127-010-9676-8
- Schneider, S. (2004). Organized crime, money laundering, and the real estate market in Canada. Journal of Property Research, 21(2), 99-118. https://doi.org/10.1080/0959991042000328801
- Serban, C. I., Mocanu, S., Popa, C., & Saru, D. (2019). Dedicated algorithms for risk minimization in suspicious financial transactions. In Proceedings of the 2019 International Conference on Computer Science and Control Systems (CSCS) (pp. 586-591). IEEE. https://doi.org/10.1109/CSCS.2019.00107
- Shadrooh, S., & Norvag, K. (2024). SMoTeF: Smurf money laundering detection using temporal order and flow analysis. Applied Intelligence, 54(15-16), 7461-7478. https://doi.org/10.1007/s10489-024-05545-4
- Sheu, G.-Y., & Li, C.-Y. (2021). On the potential of a graph attention network in money laundering detection. Journal of Money Laundering Control, 25(3), 594-608. https://doi.org/10.1108/JMLC-07-2021-0076
- Sigetova, K., Uzikova, L., Dotsenko, T., & Boyko, A. (2022). Recent trends in the financial crime of the world. Financial and Credit Activity: Problems of Theory and Practice, 5(46), 258-270. https://doi.org/10.55643/fcaptp.5.46.2022.3897
- Skinner, C. P. (2023). Coins, cross-border payments, and anti-money laundering law. Harvard Journal on Legislation, 60, 302-355. https://doi.org/10.2139/ssrn.4215481
- Solaiman, S. M. (2016). Black money, ‘white’ owners, and ‘blue’ tenants in the Bangladesh housing market: Where corruption makes the difference as protectors turn predators. Journal of Financial Crime, 23(2), 501-526. https://doi.org/10.1108/JFC-02-2015-0009
- Soudijn, M. R. J. (2019). Using police reports to monitor money laundering developments: Continuity and change in 12 years of Dutch money laundering crime pattern analyses. European Journal on Criminal Policy and Research, 25(1), 83-97. https://doi.org/10.1007/s10610-018-9379-0
- Sousa Lima, R., Marques Serrano, A. L., Onome Imoniana, J., & Medeiros Cupertino, C. (2022). Identifying financial patterns of money laundering with social network analysis: A Brazilian case study. Journal of Money Laundering Control, 25(1), 118-134. https://doi.org/10.1108/JMLC-12-2020-0139
- Tertychnyi, P., Godgildieva, M., Dumas, M., & Ollikainen, M. (2022). Time-aware and interpretable predictive monitoring system for Anti-Money Laundering. Machine Learning with Applications, 8, 100306. https://doi.org/10.1016/j.mlwa.2022.100306
- Tharani, J. S., Hóu, Z., Charles, E. Y. A., Rathore, P., Palaniswami, M., & Muthukkumarasamy, V. (2024). Unified feature engineering for detection of malicious entities in blockchain networks. IEEE Transactions on Information Forensics and Security, 19, 8924-8938. https://doi.org/10.1109/TIFS.2024.3412421
- Tiwari, M., Ferrill, J., & Allan, D. M. C. (2024a). Trade-based money laundering: A systematic literature review. Journal of Accounting Literature, 47(5), 1-26. https://doi.org/10.1108/JAL-11-2022-0111
- 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
- Tiwari, M., Gepp, A., & Kumar, K. (2021). Global money laundering appeal index: Application of principal component analysis. Journal of Money Laundering Control, 26(1), 205-211. https://doi.org/10.1108/JMLC-10-2021-0108
- Tiwari, M., Lupton, C., Bernot, A., & Halteh, K. (2024b). The cryptocurrency conundrum: The emerging role of digital currencies in geopolitical conflicts. Journal of Financial Crime, 31(6), 1622-1634. https://doi.org/10.1108/JFC-12-2023-0306
- Toan, B. H. (2022). Effects of foreign direct investment on trade-based money laundering: The case of Vietnam. Cogent Social Sciences, 8(1). https://doi.org/10.1080/23311886.2022.2132672
- Tran, Q. P. (2024). Is remittance cost a driver of trade misinvoicing? A case study of Vietnam. Journal of Economics and Development, 26(4), 362-382. https://doi.org/10.1108/JED-04-2024-0158
- Tropina, T. (2014). Fighting money laundering in the age of online banking, virtual currencies and internet gambling. ERA Forum, 15(1), 69-84. https://doi.org/10.1007/s12027-014-0335-2
- Tsingou, E. (2018). New governors on the block: The rise of anti-money laundering professionals. Crime, Law and Social Change, 69(2), 191-205. https://doi.org/10.1007/s10611-017-9751-x
- Turksen, U., Benson, V., & Adamyk, B. (2024). Legal implications of automated suspicious transaction monitoring: Enhancing integrity of AI. Journal of Banking Regulation, 25(1), 359-377. https://doi.org/10.1057/s41261-024-00233-2
- Umar, B. (2023). Effects of trade misinvoicing on money laundering in developing economies. Journal of Money Laundering Control, 26(1), 60-68. https://doi.org/10.1108/JMLC-11-2021-0124
- Umar, B., Abu, M. M., & Mohammed, Z. (2020). Strategies for preventing illicit financial flows in developing countries. Journal of Money Laundering Control, 23(3), 601-608. https://doi.org/10.1108/JMLC-02-2020-0017
- Unger, B. (2013). Can money laundering decrease? Public Finance Review, 41(5), 658-676. https://doi.org/10.1177/1091142113483353
- Unger, B., & Van Waarden, F. (2009). How to dodge drowning in data? Rule- and risk-based anti money laundering policies compared. Review of Law and Economics, 5(2), 953-985. https://doi.org/10.2202/1555-5879.1423
- United Nations Office on Drugs and Crime. (2004). United Nations convention against transnational organized crime and the protocols thereto. https://www.unodc.org/documents/middleeastandnorthafrica/organised-crime/UNITED_NATIONS_CONVENTION_AGAINST_TRANSNATIONAL_ORGANIZED_CRIME_AND_THE_PROTOCOLS_THERETO.pdf
- Vail, N. (2018). Cracking shells: The Panama Papers & looking to the European Union’s Anti-Money Laundering Directive as a framework for implementing a multilateral agreement to combat the harmful effects of shell companies. Texas A&M Law Review, 5(1), 133-153. https://doi.org/10.37419/LR.V5.I1.4
- Vassallo, D., Vella, V., & Ellul, J. (2021). Application of gradient boosting algorithms for anti-money laundering in cryptocurrencies. SN Computer Science, 2, 143. https://doi.org/10.1007/s42979-021-00558-z
- Verrier, M. (2020). Cutting the flow: Argentina as a success case against drug trafficking 2016–2019. Journal of Illicit Economies and Development, 2(1), 21-28. https://doi.org/10.31389/jied.61
- Walker, J. (1999). How big is global money laundering? Journal of Money Laundering Control, 3(1), 25-37. https://doi.org/10.1108/eb027208
- Walker, J., & Unger, B. (2009). Measuring global money laundering: ‘The Walker gravity model’. Review of Law and Economics, 5(2), 821-853. https://doi.org/10.2202/1555-5879.1418
- Wan, F., & Li, P. (2024). A novel money laundering prediction model based on a dynamic graph convolutional neural network and long short-term memory. Symmetry, 16(3), 378. https://doi.org/10.3390/sym16030378
- Wang, S.-N., & Yang, J.-G. (2007). A money laundering risk evaluation method based on decision tree. In Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 7, pp. 283-286). https://doi.org/10.1109/ICMLC.2007.4370155
- Warikandwa, T. V. (2023). Fighting fisheries crimes in the fisheries industry: Practical training reflections of the efficacy of Namibia’s fisheries law enforcement. Cogent Social Sciences, 9(2), 2286043. https://doi.org/10.1080/23311886.2023.2286043
- Woda, K. (2007). The analysis of money laundering techniq. In L. Janczewski & A. Colarik (Eds.), Cyber warfare and cyber terrorism (pp. 138-145). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-59140-991-5.ch018
- Wronka, C. (2022). Money laundering through cryptocurrencies – analysis of the phenomenon and appropriate prevention measures. Journal of Money Laundering Control, 25(1), 79-94. https://doi.org/10.1108/JMLC-02-2021-0017
- Xia, P., Ni, Z., Xiao, H., Zhu, X., & Peng, P. (2022). A novel spatiotemporal prediction approach based on graph convolution neural networks and long short-term memory for money laundering fraud. Arabian Journal for Science and Engineering, 47(2), 1921-1937. https://doi.org/10.1007/s13369-021-06116-2
- Yang, G., Liu, X., & Li, B. (2023). Anti-money laundering supervision by intelligent algorithm. Computers and Security, 132, 103344. https://doi.org/10.1016/j.cose.2023.103344
- Yarovenko, H., Vasilyeva, T., Ustinovichius, L., & Remsei, S. (2024). Illicit practices: Experience of developed countries. Journal of International Studies, 17(2), 146-177. https://doi.org/10.14254/2071-8330.2024/17-2/8
- Yim, F. H.-C., & Lee, I. P. (2018). Updates on Hong Kong’s anti-money laundering laws. Journal of Money Laundering Control, 21(3), 290-296. https://doi.org/10.1108/JMLC-07-2017-0028
Language: English
Page range: 257 - 298
Submitted on: Nov 16, 2024
Accepted on: May 16, 2025
Published on: Jun 23, 2025
Published by: University of Economics in Katowice
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Related subjects:
© 2025 Anna Popik-Mazur, published by University of Economics in Katowice
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.