Money laundering rate modelling - J. M. Beketnova; E. S. Anisimov (Вестник университета)
The article continues the analysis of the work of the model developed by the authors of forecasting money laundering rate. The purpose of the study is to compare the results of the forecast with the actual data. Authors used official data by the Ministry of Internal Affairs of the Russian Federation on economic crime rate for the period January 2011 – July 2022. In the last published work, a forecast was formed based on the seasonal integrated autoregression – moving average (SARIMA) model for 11 months of 2022 from February to December. This forecast for the past 6 months has been verified by comparison with the actual values by calculating the relative error and the RMSE (root mean square error). In comparison, the authors noted a rare distribution of registered cases for the last decade: an excess of the number of crimes in May over June. The result of the study may be used as an auxiliary tool in the analytical complexes of subjects of financial monitoring and supervisory authorities. The results reflected the high accuracy of the forecasts obtained for the sum of the periods, and the conclusion was also made about the importance of observing the seasonal structure of the dynamics of crimes for operational changes in the parameters of the model.