Assessing the maturity of the current global system for combating financial and cyber fraud

Olha Kuzmenko; Hanna Yarovenko; Larysa Perkhun

doi:https://doi.org/10.59170/stattrans-2023-013

Assessing the maturity of the current global system for combating financial and cyber fraud

Olha Kuzmenko Sumy State University, Ukraine. ORCID:https://orcid.org/0000- 0001-8520-2266 , Hanna Yarovenko Sumy State University, Ukraine; University Carlos III of Madrid, Spain. ORCID:https://orcid.org/0000-0002-8760-6835 , Larysa Perkhun MHP SE, Ukraine. ORCID:https://orcid.org/0000-0002-8667-2312 Statistics in Transition new series, vol. 24, 2023, 1, pages: 229-258 Published online: 24 February 2023 https://doi.org/10.59170/stattrans-2023-013

273 Views 41 Downloads

ARTICLE

(English) PDF

ABSTRACT

The purpose of the article is to assess the maturity of systems for counteracting financial and cyber fraud with the view of their future integration at global-level. The calculations made by the authors were based on indicators for 76 countries, which characterized each country's level of cybersecurity and its ability to combat financial fraud in 2018. After optimising the input data and selecting relevant indicators, the authors built an integrated cybersecurity index using the Sundarovsky convolution method. Sigma-limited parameterisation and Pareto-optimisation were then used to identify the determinants of the ability to counter financial and cyber fraud, which were used as predictors. Nonlinear regression was applied to determine the dependency of the integrated cybersecurity index on the government efficiency index, the ease of doing business and on the crime indices. On this basis, the authors conducted a bifurcation analysis of the maturity of current global system for combating financial and cyber fraud and produced its phase portraits. It was found to be mature (“Government Efficiency Index – Ease of Doing Business” and “Ease of Doing Business – Crime Index”) and insufficient mature (“Government Efficiency Index – Crime Index”), with the components' imbalance indicating high system's sensitivity to react on changes. The constructed 'Equilibrium States' phase portraits showed non-equilibrium phase portraits of the 'saddle' type. The obtained results made it possible to identify determinants of a global integrated system's instability to combat financial and cyber fraud.

KEYWORDS

financial fraud, cyber fraud, phase portrait, bifurcation analysis.

REFERENCES

Akhramovych, V., Shuklin, G., Pepa, Y., Muzhanova, T. and Zozulia, S., (2022). Devising a procedure to determine the level of informational space security in social networks considering interrelations among users. Eastern-European Journal of Enterprise Technologies, [e-journal], 1(9–115), pp. 63–74; 10.15587/1729- 4061.2022.252135.

Amala Dhaya, M. D. and Ravi, R., (2021). Multi feature behavior approximation model based efficient botnet detection to mitigate financial frauds. Journal of Ambient Intelligence and Humanized Computing, [e-journal], 12(3), pp. 3799–3806; 10.1007/s12652-020-01677-w.

Aziz, R. M., Baluch, M. F., Patel, S. and Ganie, A. H., (2022). LGBM: a machine learning approach for Ethereum fraud detection. International Journal of Information Technology (Singapore), [e-journal]; 10.1007/s41870-022-00864-6

BBC, (2022). Ukraine cyber-attack: Russia to blame for hack, says Kyiv. [online] Available at: https://www.bbc.com/news/world-europe-59992531 [Accessed 31 May 2022].

Bystray, G. P., Lykov, I. A. and Nikulina, N. L., (2012). Risks assessment and forecasting long time rows of economic indicators. Economy of Region, [e-journal] 3, pp. 240–249; 10.17059/2012-3-24.

CyberPeace Institute, (2022). Ukraine: Timeline of Cyberattacks on critical infrastructure and civilian objects. [online] Available at: https://cyberpeaceinstitute.org/ukraine-timeline-of-cyberattacks/, [Accessed 31 May 2022].

Day, M.-Y., (2021). Artificial intelligence for knowledge graphs of cryptocurrency antimoney laundering in fintech. In: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021. Virtual, Online, 8 November 2021. New York: Association for Computing Machinery; 10.1145/3487351.3488415.

Deloitte, (2019). The connected defense: Elevating the fight against financial crime. Using 4IR technologies to prevent and detect the growing ecosystem of financial crime. [online] Available at: https://www2.deloitte.com/content/dam/Deloitte/nl/Documents/financialservices/ deloitte-nl-fsi-elevating-the-fight-against-financial-crime.pdf, [Accessed 31 May 2022].

e-Governance Academy Foundation, (2022). National Cyber Security Index. [online] Available at: https://ncsi.ega.ee/ncsi-index/, [Accessed 31 May 2022].

Euronews, (2022). Ukraine's defence ministry and two banks targeted in cyberattack. [online] Available at: https://www.euronews.com/my-europe/2022/02/15/ukraines- defence-ministry-and-two-banks-targeted-in-cyberattack, [Accessed 31 May 2022].

FinCEN, (2009). Mortgage Loan Fraud Connections with Other Financial Crime: An Evaluation of Suspicious Activity Reports Filed By Money Services Businesses, Securities and Futures Firms, Insurance Companies and Casinos. [online] Available at: https://www.fincen.gov/sites/default/files/shared/ mortgage_fraud.pdf, [Accessed 31 May 2022].

Granados, O. and Garcia-Bedoya, O., (2022). Deep Learning-Based Facial Recognition on Hybrid Architecture for Financial Services. In S. Misra, A.K. Tyagi, V. Piuri, L. Garg, ed. 2022. Artificial Intelligence for Cloud and Edge Computing. Springer: Cham. pp. 51-70. 10.1007/978-3-030-80821-1_3.

Granados, O., Vargas, A., (2022). The geometry of suspicious money laundering activities in financial networks. EPJ Data Science, [e-journal], 11(1), article number 6; 10.1140/epjds/s13688-022-00318-w.

Hayble-Gomes, E., (2022). The use of predictive modeling to identify relevant features for suspicious activity reporting. Journal of Money Laundering Control, [e-journal] ahead-of-print; 10.1108/JMLC-02-2022-0034.

Idowu, B. A., Vaidyanathan, S., Sambas, A., Olusola, O. I. and Onma, O. S., (2018). A new chaotic finance system: Its analysis, control, synchronization and circuit design. In: V.-T. Pham, S. Vaidyanathan, C. Volos, T. Kapitaniak, ed. 2018. Nonlinear Dynamical Systems with Self-Excited and Hidden Attractors. Springer: Cham, pp. 271–295; 10.1007/978-3-319-71243-7_12.

Krebsonsecurity, (2022). Report: Recent 10x Increase in Cyberattacks on Ukraine. [online] Available at: https://krebsonsecurity.com/2022/03/report-recent-10x-increase-in-cyberattackson- ukraine/, [Accessed 31 May 2022].

Kuzmenko, ?., Yarovenko, H. and Radko, V., (2021). Poperednii analiz protsesu konverhentsii system kiberbezpeky ta finansovoho monitorynhu krain [Preliminary analysis of the convergence process of cyber security systems and financial monitoring of countries. Economy and society], [e-journal], 32; 10.32782/2524-0072/2021-32-37. [in Ukrainian].

Li, H., Wong, M.-L., (2021). Grammar-Based Multi-objective Genetic Programming with Token Competition and Its Applications in Financial Fraud Detection. Natural Computing Series, [e-journal], pp. 259–285; 10.1007/978-3-030-79553- 5_11.

Mahootiha, M., Golpayegani, A.H. and Sadeghian, B., (2021). Designing a New Method for Detecting Money Laundering based on Social Network Analysis. In: 26th International Computer Conference, Computer Society of Iran, 26th International Computer Conference, Computer Society of Iran, CSICC 2021. Tehran, 3 March 2021. New York: Institute of Electrical and Electronics Engineers (IEEE); 10.1109/CSICC52343.2021.9420621.

Matanky-Becker, R., Cockbain, E., (2022). Behind the criminal economy: using UK tax fraud investigations to understand money laundering myths and models. Crime, Law and Social Change, [e-journal], 77(4), pp. 405-429; 10.1007/s10611-021- 09997-4.

Netzwerk Steuergerechtigkeit, (2022). Financial Secrecy Index - 2018 Results. [online] Available at: https://www.netzwerk-steuergerechtigkeit.de/wp-content/uploads/2018/01/6_fsiranking_ incl-_eu-tax-havens.pdf, [Accessed 31 May 2022].

OCHA, (2018). Global Terrorism Index (2018). [online] Available at: https://reliefweb.int/report/world/global-terrorism-index-2018, [Accessed 31 May 2022].

Perkhun, L., Sorochynskyi, O., Izosimov, O., (2015). Systema vzaiemodii shakhraiskykh atak ta instrumentiv borotby z nymy. Scientific Bulletin of National Academy of Statistics, Accounting and Audit, [e-journal], 1, pp. 50–65; http://194.44.12.92:8080/ jspui/handle/123456789/3813. [in Ukrainian].

PwC, (2018). Building a united front on financial crimes. [online] Available at: https://www.pwc.com/gx/en/financial-services/pdf/united-front-financial-crimes- 2018-pwc.pdf, [Accessed 31 May 2022].

Riani, M., Corbellini, A. and Atkinson, A.C., (2018). The Use of Prior Information in Very Robust Regression for Fraud Detection. International Statistical Review, [e-journal], 86(2), pp. 205–218; 10.1111/insr.12247.

Rouhollahi, Z., Beheshti, A., Mousaeirad, S. and Goluguri, S., (2021). Towards Proactive Financial Crime and Fraud Detection through Artificial Intelligence and

RegTech Technologies. In: ACM International Conference Proceeding Series, 23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021. Virtual, Online, 29 November 2021 through 1 December 2021. New York: Association for Computing Machinery; 10.1145/3487664.3487740.

Sierikov, A. V., Zubova, O.O., (2010). Marketing as a necessary condition for synergetic management of economic activity. Actual Problems of Economics, 5, pp. 276–283.

Tharani, J.S., Charles, E.Y.A., Hou, Z., Palaniswami, M., and Muthukkumarasamy, V., (2021). In: Proceedings - Conference on Local Computer Networks, 46th IEEE Conference on Local Computer Networks, LCN 2021. Edmonton, 4 October 2021. New York: Institute of Electrical and Electronics Engineers (IEEE); 10.1109/LCN52139.2021.9524878.

The GlobalEconomy, (2022). List of available indicators. [online] Available at: https://www.theglobaleconomy.com/indicators_list.php, [Accessed 31 May 2022].

The World Bank, (2022). World Development Indicators; [online] Available at: https://databank.worldbank.org/reports.aspx?source=2&series=SI.POV.GINI&co untry=#, [Accessed 31 May 2022].

Tundis, A., Nemalikanti, S. and Mühlhäuser, M., (2021). In: ACM International Conference Proceeding Series, 16th International Conference on Availability, Reliability and Security, ARES. Virtual, Online, 17 August 2021. New York: Association for Computing Machinery; 10.1145/3465481.3469196.

Wilkens, K., Thomas, N. and Fofana, M.S., (2004). Stability of technology stock prices: Evidence of rational expectations or irrational sentiment? Managerial Finance, [e-journal] 30(12), pp. 33–54; 10.1108/03074350410769380.

Xia, P., Ni, Z., Zhu, X., He, Q. and Chen, Q., (2022). A novel prediction model based on long short-term memory optimised by dynamic evolutionary glowworm swarm optimisation for money laundering risk. International Journal of Bio-Inspired Computation, [e-journal], 19(2), pp. 77–86; 10.1504/IJBIC.2022.121233.

Yang, Y. and Wu, M., (2021). Explainable Machine Learning for Improving Logistic Regression Models. In: IEEE International Conference on Industrial Informatics, 19th IEEE International Conference on Industrial Informatics, INDIN 2021. Mallorca, 21 July 2021. New York: Institute of Electrical and Electronics Engineers (IEEE); 10.1109/INDIN45523.2021.9557392.

Yarovenko, H., (2021). Informatsiina bezpeka yak draiver rozvytku natsionalnoi ekonomiky | Information security as a driver of national economy development. DSc. Sumy State University. Available at: essuir.sumdu.edu.ua/handle/123456789/83664, [Accessed 31.05.2022]. [in Ukrainian].