The triple threat: Understanding the effects of cyber threats, corruption, and money laundering on the business environment

Abstract

Research background: Cyber threats, corruption and money laundering are interconnected factors that pose significant challenges to the business environment. Their impact varies based on a country's economic development and the effectiveness of countermeasures. Despite global and national efforts to combat these threats, their combined influence on business conditions requires further examination.

Purpose of the article: This study aims to analyse the impact of cyber threats, corruption, and money laundering on the business environment across different country groups. It identifies the most vulnerable aspects of business to triple threats and highlights countries exhibiting anomalies in their ability to counter these illegal practices.

Methods: The research utilises data from 125 countries, incorporating the Basel Anti-Money Laundering Index, the National Cyber Security Index, and the Corruption Perceptions Index. Correlation analysis established statistically significant relationships between these threats and business conditions. Cluster analysis identified three country groups based on GDP, ease of doing business, and countermeasure effectiveness. Canonical analysis determined the most affected business sectors, while neural network modelling revealed countries with exceptionally high or low effectiveness in combating these threats.

Findings & value added: The most affected areas include tax payments, international trade, contract enforcement, electricity access, and insolvency procedures. Among developed countries, Denmark, Finland, and Norway demonstrate high effectiveness in countering these threats, whereas Bulgaria, Cyprus, and Greece show lower efficiency. In developing nations, China, Thailand, and Kazakhstan exhibit strong countermeasures, while Egypt, Ghana, and Grenada lag behind. Among the least developed countries, Mozambique and Nicaragua show high effectiveness, while Venezuela and Yemen fall into the low-performance category. These findings provide a foundation for enhancing national policies and strategies to strengthen economic security and resilience against financial crimes and cyber threats.

Keywords

cyber threats, corruption, money laundering, business environment

References

1. Androniceanu, A. (2023). The new trends of digital transformation and artificial intelligence in public administration. Administratie si Management Public, 40, 147–155. DOI: https://doi.org/10.24818/amp/2023.40-09
   View in Google Scholar
2. Arroyabe, M. F., Arranz, C. F., de Arroyabe, I. F., & de Arroyabe, J. C. F. (2024). The effect of IT security issues on implementing industry 4.0 in SMEs: Barriers and challenges. Technological Forecasting and Social Change, 199, 123051. DOI: https://doi.org/10.1016/j.techfore.2023.123051
   View in Google Scholar
3. Asare, K., & Samusevych, Y. (2023). Exploring financial fraud, tax tools, and economic security research: Comprehensive bibliometric analysis. Financial Markets, Institutions and Risks, 7(3), 136–146. DOI: https://doi.org/10.61093/fmir.7(3).136-146.2023
   View in Google Scholar
4. Badur, M. M., Yılmaz, E., & Sensoy, F. (2024). Do corruption and inequality shape sustainable development? Evidence from the post-Soviet countries. International Journal of Social Economics, 51(1), 115–132. DOI: https://doi.org/10.1108/IJSE-01-2023-0065
   View in Google Scholar
5. Baker, T., & Shortland, A. (2023). Insurance and enterprise: Cyber insurance for ransomware. Geneva Papers on Risk and Insurance-Issues and Practice, 48(2), 275–299. DOI: https://doi.org/10.1057/s41288-022-00281-7
   View in Google Scholar
6. Bartulovic, M., Aljinovic, N., & Piplica, D. (2023). Determining the relationship between corruption and money laundering. Montenegrin Journal of Economics, 19(2), 109–118. DOI: https://doi.org/10.14254/1800-5845/2023.19-2.9
   View in Google Scholar
7. Basel Institute on Governance. (2023). Basel AML Index. Retrieved from https://index.baselgovernance.org/ (01.03.2024).
   View in Google Scholar
8. Bayar, Y., Gavriletea, M. D., & Remeikienė, R. (2023). Impact of the rule of law, corruption and terrorism on tourism: Empirical evidence from Mediterranean countries. Oeconomia Copernicana, 14(3), 1009–1035. DOI: https://doi.org/10.24136/oc.2023.02930
   View in Google Scholar
9. Bran, E., Rughiniș, R., Țurcanu, D., & Nadoleanu, G. (2024). Technical innovations and social implications: Mapping global research focus in AI, blockchain, cybersecurity, and privacy. Computers, 13(10), 254. DOI: https://doi.org/10.3390/computers13100254
   View in Google Scholar
10. Brychko, M., Bilan, Y., Lyeonov, S., & Streimikiene, D. (2023). Do changes in the business environment and sustainable development really matter for enhancing enterprise development? Sustainable Development, 31(2), 587–599. DOI: https://doi.org/10.1002/sd.2410
    View in Google Scholar
11. Burca, V., Popa, A. F., Sahlian, D. N., Trasca, D. L., & Bobitan, N. (2022). Modelling the impact of earnings management on the probability of financial statements fraud. Engineering Economics, 33(5), 521–539. DOI: https://doi.org/10.5755/j01.ee.33.5.30672
    View in Google Scholar
12. Cifuentes‐Faura, J. (2024). Corruption in Ukraine during the Ukrainian–Russian war: A decalogue of policies to combat it. Journal of Public Affairs, 24(1), e2905. DOI: https://doi.org/10.1002/pa.2905
    View in Google Scholar
13. D’avino, C. (2023). Money laundering and AML regulatory and judicial system regimes: Investigation of FinCEN files. European Journal of Law and Economics, 55(2), 195–223. DOI: https://doi.org/10.1007/s10657-022-09756-3
    View in Google Scholar
14. e-Governance Academy. (2023). National Cyber Security Index. Retrieved from https://ncsi.ega.ee/ncsi-index/ (01.03.2024).
    View in Google Scholar
15. eSentire. (2023). 2023 official cybercrime report. Retrieved from https://www.esentire.com/resources/library/2023-official-cybercrime-report (01.08.2024).
    View in Google Scholar
16. Europol. (2023). Money laundering. Retrieved from https://www.europol.europa.eu/crime-areas/economic-crime/money-laundering#:~:text=The%20scale%20of%20money%20laundering,and%201.87%20trillion%20each%20year (01.08.2024).
    View in Google Scholar
17. Farinha, J., & López-de-Foronda, O. (2024). The impact of corruption on investment and financing in the European Union: New insights. European Journal of Finance, 30(4), 339–344. DOI: https://doi.org/10.1080/1351847X.2023.2240846
    View in Google Scholar
18. Farrand, B., Carrapico, H., & Turobov, A. (2024). The new geopolitics of EU cybersecurity: Security, economy and sovereignty. International Affairs, 100(6), 2379–2397. DOI: https://doi.org/10.1093/ia/iiae231
    View in Google Scholar
19. Fhima, F., Nouira, R., & Sekkat, K. (2023). How does corruption affect sustainable development? A threshold non-linear analysis. Economic Analysis and Policy, 78, 505–523. DOI: https://doi.org/10.1016/j.eap.2023.03.020
    View in Google Scholar
20. Gaspareniene, L., Gagyte, G., Remeikiene, R., & Matuliene, S. (2022). Clustering of the European Union member states based on money laundering measuring indices. Economics and Sociology, 15(2), 153–171. DOI: https://doi.org/10.14254/2071-789X.2022/15-2/10
    View in Google Scholar
21. Gavenaite-Sirvydiene, J., & Miecinskiene, A. (2023). A framework for cyber security areas evaluation in financial sector. Transformations in Business & Economics, 22(3), 192–205.
    View in Google Scholar
22. Gaviyau, W., & Sibindi, A. B. (2023). Global anti-money laundering and combating terrorism financing regulatory framework: A critique. Journal of Risk and Financial Management, 16(7), 313. DOI: https://doi.org/10.3390/jrfm16070313
    View in Google Scholar
23. Ha, L. T., To, T. T., Thi Thanh Huyen, N., Hoa, H. Q., & Ngoc, T. A. (2024). The roles of e-government in combating corruption: Evidence from European countries. Journal of Science and Technology Policy Management, 15(6), 1505–1533. DOI: https://doi.org/10.1108/JSTPM-04-2022-0065
    View in Google Scholar
24. Ho, T. L., & Nguyen, M-L.T. (2023). Nonlinear effects of corruption control on growth: A threshold model. Montenegrin Journal of Economics, 19(2), 119–128. DOI: https://doi.org/10.14254/1800-5845/2023.19-2.10
    View in Google Scholar
25. Identity Theft Resource Centre. (2024). ITRC annual data breach report. Retrieved from https://www.idtheftcenter.org/publication/2023-data-breach-report/ (01.08.2024).
    View in Google Scholar
26. Ionescu, A. M., Clipa, A.-M., Turnea, E.-S., Clipa, C.-I., Bedrule-Grigoruță, M. V., & Roth, S. (2022). The impact of innovation framework conditions on corporate digital technology integration: Institutions as facilitators for sustainable digital transformation. Journal of Business Economics and Management, 23(5), 1037–1059. DOI: https://doi.org/10.3846/jbem.2022.17039
    View in Google Scholar
27. Johnson, S. (2018). Corruption is costing the global economy $3.6 trillion dollars every year. World Economic Forum. Retrieved from https://www.weforum.org/agenda/2018/12/the-global-economy-loses-3-6-trillion-to-corruption-each-year-says-u-n/ (01.08.2024).
    View in Google Scholar
28. Jones, L. A. (2024). Unveiling human factors: Aligning facets of cybersecurity leadership, insider threats, and arsonist attributes to reduce cyber risk. Socio Economic Challenges, 8(2), 44–63. DOI: https://doi.org/10.61093/sec.8(2).44-63.2024
    View in Google Scholar
29. Kuzior, A., Arefiev, S., & Poberezhna, Z. (2023a). Informatization of innovative technologies for ensuring macroeconomic trends in the conditions of a circular economy. Journal of Open Innovation: Technology, Market, and Complexity, 9(1), 10–20. DOI: https://doi.org/10.1016/j.joitmc.2023.01.001
    View in Google Scholar
30. Kuzior, A., Arefieva, O., Kovalchuk, A., Brożek, P., & Tytykalo, V. (2022a). Strategic guidelines for the intellectualization of human capital in the context of innovative transformation. Sustainability, 14(19), 11937. DOI: https://doi.org/10.3390/su141911937
    View in Google Scholar
31. Kuzior, A., Brożek, P., Kuzmenko, O., Yarovenko, H., & Vasilyeva, T. (2022b). Countering cybercrime risks in financial institutions: Forecasting information trends. Journal of Risk and Financial Management, 15(12), 613. DOI: https://doi.org/10.3390/jrfm15120613
    View in Google Scholar
32. Kuzior, A., Vasylieva, T., Kuzmenko, O., Koibichuk, V., & Brożek, P. (2022c). Global digital convergence: Impact of cybersecurity, business transparency, economic transformation, and AML efficiency. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 195. DOI: https://doi.org/10.3390/joitmc8040195
    View in Google Scholar
33. Kuzior, A., Yarovenko, H., Brożek, P., Sidelnyk, N., Boyko, A. & Vasilyeva, T. (2023b). Company cybersecurity system: Assessment, risks and expectations. Production Engineering Archives, 29(4), 379–392. DOI: https://doi.org/10.30657/pea.2023.29.43
    View in Google Scholar
34. Kuzmenko, O., Bilan, Y., Bondarenko, E., Gavurova, B., & Yarovenko, H. (2023). Dynamic stability of the financial monitoring system: Intellectual analysis. PLoS ONE, 18(1), e0276533. DOI: https://doi.org/10.1371/journal.pone.0276533
    View in Google Scholar
35. Kuzmenko, O., Šuleř, P., Lyeonov, S., Judrupa, I., & Boiko, A. (2020). Data mining and bifurcation analysis of the risk of money laundering with the involvement of financial institutions. Journal of International Studies, 13(3), 332–339. DOI: https://doi.org/10.14254/2071-8330.2020/13-3/22
    View in Google Scholar
36. Lăzăroiu, G., Bogdan, M., Geamănu, M., Hurloiu, L., Luminița, L., & Ștefănescu, R. (2023). Artificial intelligence algorithms and cloud computing technologies in blockchain-based fintech management. Oeconomia Copernicana, 14(3), 707–730. DOI: https://doi.org/10.24136/oc.2023.021
    View in Google Scholar
37. Lazic, M. (2023). 27 informative money laundering statistics in 2023. Legal Jobs. Retrieved from https://legaljobs.io/blog/money-laundering-statistics (01.08.2024).
    View in Google Scholar
38. Levy, Y., & Gafni, R. (2021). Introducing the concept of cybersecurity footprint. Information & Computer Security, 29(5), 724–736. DOI: https://doi.org/10.1108/ICS-04-2020-0054
    View in Google Scholar
39. Li, T., & Walton, S. (2023). Business strategy and cybersecurity breaches. Journal of Information Systems, 37(2), 51–76. DOI: https://doi.org/10.2308/ISYS-2022-033
    View in Google Scholar
40. Linhartová, V., & Halásková, M. (2022). Determinants of corruption: A panel data analysis of Visegrad countries. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(1), 51–79. DOI: https://doi.org/10.24136/eq.2022.003
    View in Google Scholar
41. Liu, C., & Babar, M. A. (2024). Corporate cybersecurity risk and data breaches: A systematic review of empirical research. Australian Journal of Management, 03128962241293658. DOI: https://doi.org/10.1177/03128962241293658
    View in Google Scholar
42. Lozano Almansa, J. M., Tarifa Fernández, J., & Sánchez Pérez, A. M. (2023). Digital transformation and real options: Evaluating the investment in Cloud ERP. Inzinerine Ekonomika-Engineering Economics, 34(4), 397–411. DOI: https://doi.org/10.5755/j01.ee.34.4.30678
    View in Google Scholar
43. Lubis, H. Z., Sari, M., Ramadhany, A. A., Ovami, D. C., & Brutu, I. R. (2024). Effect of internal audit, internal control, and audit quality on fraud prevention: Evidence from the public sector in Indonesia. Problems and Perspectives in Management, 22(2), 40–50. DOI: https://doi.org/10.21511/ppm.22(2).2024.04
    View in Google Scholar
44. Mazurenko, O., Tiutiunyk, I., Cherba, V., Artyukhov, A., & Yehorova, Y. (2023). Shadow tax evasion and its impact on the competitiveness of the country’s tax system. Public and Municipal Finance, 12(2), 129–142. http://dx.doi.org/10.21511/pmf.12(2).2023.11. DOI: https://doi.org/10.21511/pmf.12(2).2023.11
    View in Google Scholar
45. Mentzer, M. S. (2024). Political culture and the resource curse: Public sector corruption across the United States. Social Responsibility Journal, 20(10), 2084–2099. DOI: https://doi.org/10.1108/SRJ-09-2023-0508
    View in Google Scholar
46. Murrar, F. (2022). Fraud schemes during COVID-19: A comparison from FATF countries. Journal of Financial Crime, 29(2), 533–540. DOI: https://doi.org/10.1108/JFC-09-2021-0203
    View in Google Scholar
47. Omotoye, A. M., & Holtzhausen, N. (2025). Integration of digital technologies in anti-corruption initiatives in Botswana: Lessons from Georgia, Ukraine and South Africa. Transforming Government: People, Process and Policy, 19(1), 22–36. DOI: https://doi.org/10.1108/TG-01-2024-0002
    View in Google Scholar
48. Pavlidis, G. (2023). Deploying artificial intelligence for anti-money laundering and asset recovery: The dawn of a new era. Journal of Money Laundering Control, 26(7), 155–166. DOI: https://doi.org/10.1108/JMLC-03-2023-0050
    View in Google Scholar
49. Pigola, A., Da Costa, P. R., Ferasso, M., & da Silva, L. F. C. (2024). Enhancing cybersecurity capability investments: Evidence from an experiment. Technology in Society, 76, 102449. DOI: https://doi.org/10.1016/j.techsoc.2023.102449
    View in Google Scholar
50. Raad, A. N., & Hewavidana, S. (2024). Transparency international releases 2023 Corruption Perceptions Index: Denmark tops the ranking, UK hits historically low score. Ropes & Gray. Retrieved from https://www.ropesgray.com/en/insights/viewpoints/102iyiv/transparency-international-releases-2023-corruption-perceptions-index-denmark-to (01.08.2024).
    View in Google Scholar
51. Remeikienė, R., & Gasparėnienė, L. (2022). Potential for increasing the efficiency of public administration in municipal institutions based on the relationship between the unemployment rate and the shadow economy. Administratie si Management Public, 38, 6–30.
    View in Google Scholar
52. Remeikienė, R., Ligita, G., Fedajev, A., Raistenskis, E., & Krivins, A. (2022). Links between crime and economic development: EU classification. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(4), 909–938. DOI: https://doi.org/10.24136/eq.2022.031
    View in Google Scholar
53. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics, 20, 53–65. DOI: https://doi.org/10.1016/0377-0427(87)90125-7
    View in Google Scholar
54. Sarhan, A. A., & Al‐Najjar, B. (2023). What drives firms' commitment to fighting corruption? Evidence from the UK. Journal of International Financial Management & Accounting, 34(3), 791–825. DOI: https://doi.org/10.1111/jifm.12188
    View in Google Scholar
55. Smaili, N., Radu, C., & Khalili, A. (2023). Board effectiveness and cybersecurity disclosure. Journal of Management and Governance, 27(4), 1049–1071. DOI: https://doi.org/10.1007/s10997-022-09637-6
    View in Google Scholar
56. Smartsearch. (2024). 2024 Money laundering and financial crime report. Retrieved from https://www.smartsearch.com/solutions/2024-money-laundering-and-financial-crime-report (01.08.2024).
    View in Google Scholar
57. Song, U., Hur, G., Lee, S., & Park, J. (2024). Unravelling the dynamics of the cyber threat landscape: Major shifts examined through the recent societal events. Sustainable Cities and Society, 103, 105265. DOI: https://doi.org/10.1016/j.scs.2024.105265
    View in Google Scholar
58. Statista. (2023). Average cost of a data breach worldwide from May 2020 to March 2023, by industry (in million U.S. dollars). Retrieved from https://www.statista.com/statistics/387861/cost-data-breach-by-industry/ (01.08.2024).
    View in Google Scholar
59. Straková, J., Talíř, M., & Váchal, J. (2022). Opportunities and threats of digital transformation of business models in SMEs. Economics and Sociology, 15(3), 159–171. DOI: https://doi.org/10.14254/2071-789X.2022/15-3/9
    View in Google Scholar
60. Sufi, F. (2024). A new time series dataset for cyber-threat correlation, regression and neural-network-based forecasting. Information, 15(4), 199. DOI: https://doi.org/10.3390/info15040199
    View in Google Scholar
61. Sytnik, I., Stopochkin, A., Wielki, J., Edaich, S., & Sytnik, B. (2023). Prerequisites and scenarios for the EU economy transformation in light of Industry 4.0 development. Journal of International Studies, 16(2), 85–101. DOI: https://doi.org/10.14254/2071-8330.2023/16-2/5
    View in Google Scholar
62. Tosza, S. (2024). Enforcement of international sanctions as the third pillar of the anti-money laundering framework. An unannounced effect of the AML reform and the Sanctions Directive. New Journal of European Criminal Law, 15(3), 336–356. DOI: https://doi.org/10.1177/20322844241274166
    View in Google Scholar
63. Transparency International. (2023). Corruption Perceptions Index. Retrieved from https://www.transparency.org/en/cpi/2022 (01.03.2024).
    View in Google Scholar
64. United Nations. (2023). Goal 16: Promote just, peaceful and inclusive societies. Retrieved from https://www.un.org/sustainabledevelopment/peace-justice/ (01.08.2024).
    View in Google Scholar
65. Utkina, M. (2023). Leveraging blockchain technology for enhancing financial monitoring: Main challenges and opportunities. European Journal of Interdisciplinary Studies, 15(2), 134–151. DOI: https://doi.org/10.24818/ejis.2023.21
    View in Google Scholar
66. World Bank Group. (2023). Ease of doing business rankings. Retrieved from https://archive.doingbusiness.org/en/rankings (01.03.2024).
    View in Google Scholar
67. World Economic Forum. (2023). The global risks report 2023 18th edition.
    View in Google Scholar
68. Yarovenko, H., Lyeonov, S., Wojcieszek, K. A., & Szira, Z. (2023). Do IT users behave responsibly in terms of cybercrime protection?. Human Technology, 19(2), 178–206. DOI: https://doi.org/10.14254/1795-6889.2023.19-2.3
    View in Google Scholar
69. Yin, L., Sun, G., & Kong, T. (2025). Regional big data development and corporate financial fraud. Pacific-Basin Finance Journal, 90, 102693. DOI: https://doi.org/10.1016/j.pacfin.2025.102693
    View in Google Scholar
70. Zafar, S., Rahman, I. U., & Ammara, S. (2023). Disasters and corruption: An empirical analysis of 16 countries from Asia and the Middle East. International Journal of Disaster Risk Reduction, 90, 103678. DOI: https://doi.org/10.1016/j.ijdrr.2023.103678
    View in Google Scholar