Development of a model for predicting money laundering rate
https://doi.org/10.26425/1816-4277-2022-5-136-143
Abstract
The article suggests model for predicting the level of money laundering on the basis of data from the Ministry of Internal Affairs of the Russian Federation on the state of economic crime in Russia since the beginning of 2011. Using a seasonally integrated autoregressive moving average (SARIMA) model, it compares different regression models for the research tasks (linear regression, logistic regression, autoregressive and SARIMA). The necessity of taking into account seasonal regularities in the structure of money laundering was underlined, and the SARIMA model with the lowest deviations from the actual values was chosen. The necessity of taking into account seasonal regularities in the structure of money laundering was underlined, and the SARIMA model with the lowest deviations from the actual values was chosen. The article presents the results of data analysis using the method of least squares, calculating the mean squared error (MSE). High accuracy of short-term forecasts was noted: the deviation from the actual number of cases is about three cases (with the average number of cases being 68 over the last 10 years). The forecasting model can be recommended for implementation in the analytical complexes of financial monitoring and supervisory authorities.
About the Authors
E. S. Anisimov
Financial University
Russian Federation
Russian Federation
Efim S. Anisimov, Student
Moscow
J. M. Beketnova
Financial University
Russian Federation
Russian Federation
Julia M. Beketnova, Cand. Sci. (Tech.), Assoc. Рrof. at the Information Security Department
Moscow
References
1. Rocha-Salazar J.-d.-J., Segovia-Vargas M.-J., Camacho-Miñano M.-d.-M. Money laundering and terrorism financing detection using neural networks and an abnormality indicator. Expert Systems with Applications. 2021;169:114470. https://doi.org/10.1016/j.eswa.2020.114470
2. Shalagin A.E., Idiyatullov A.D. Transformation of Crime in the XXI Century: Features of Prevention and Counteraction. Bulletin of the Kazan Law Institute of the Ministry of Internal Affairs of Russia. 2021;12(2):227–235. https://doi.org/10.37973/KUI.2021.95.12.016
3. Robertson D.L., Goodridge W.A. Predicting density of serious crime incidents using a Multiple-Input Hidden Markov Maximization a posteriori model. Machine Learning with Applications. 2022;7:100231. http://dx.doi.org/10.1016/j.mlwa.2021.100231
4. Hajela G., Chawla M., Rasool A. A Clustering Based Hotspot Identification Approach For Crime Prediction. Procedia Computer Science. 2020;167:1462–1470. https://doi.org/10.1016/j.procs.2020.03.357
5. Basel Committee Recommendations “Know your customer “. Customer due diligence for banks (Basel Committee on Banking Supervision standards). https://docs.cntd.ru/document/901934950 (accessed: 26.03.2022).
6. “Know your customer” platform will reduce the burden on bona fide entrepreneurs. http://www.cbr.ru/press/event/?id=12411 (accessed 06.03.2022).
7. ArunKumar K.E., Kalaga D.V., Mohan Sai Kumar Ch., Chilkoor G., Kawaji M., Brenza T.M. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Applied Soft Computing. 2021;103:107161. https://doi.org/10.1016/j.asoc.2021.107161
8. Farsi M., Hosahalli D., Manjunatha B.R., Gad I., Atlam El-S., Ahmed A., Elmarhomy G., Elmarhoumy M., Ghoneim O.A. Parallel genetic algorithms for optimizing the SARIMA model for better forecating of the NCDC weather data. Alexandria Engineering Journal. 2021;60(1):1299–1316. https://doi.org/10.1016/j.aej.2020.10.052
9. Mao Q., Zhang K., Yan W., Cheng C. Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. Journal of Infection and Public Health. 2018;11(5):707–712. https://doi.org/10.1016/j.jiph.2018.04.009
10. The Ministry of Internal Affairs of the Russian Federation. Crime rate in Russia. https://мвд.рф/reports (accessed 06.03.2022).
Review
For citations:
Anisimov E.S., Beketnova J.M. Development of a model for predicting money laundering rate. Vestnik Universiteta. 2022;(5):136-143. (In Russ.) https://doi.org/10.26425/1816-4277-2022-5-136-143
Views:
398
Email this article 