DATA MINING-BASED ASSESSEMENT OF THE RISK OF USING FINANCIAL INTERMEDIARIES FOR MONEY LAUNDERING

Abstract

The paper deals with the mathematical economic modeling of the neural network describing the dependence of the risk of using financial intermediaries for money laundering on factors. Implementation of the proposed approach involved a multilayer perceptron (MLP) and a network based on radial basis functions (RBF). The BFGS algorithm was used to build a neural network based on the multilayer perceptron (MLP). The RBFT algorithm was used to construct a neural network based on radial basis functions (RBF). Data mining in the context of identifying key factors of the investigated risk was based on the collinearity study by applying sigma-limited parameterization and correlation analysis of the dependence of both the regressand on each of the regressors, as well as the factors among themselves. It is proposed to use the Statistica software, the Analysis package, the Advanced Methods tab, the GLM General Linear Models tab for data mining. A data set was generated for 215 countries of the world for 2017 to conduct the study. It was implemented the ranking of the predictors by the degree of their influence on the response: 1) Corruption Perceptions Index; 2) internally displaced persons, new displacement associated with conflict and violence (number of cases) 3) Happy Planet Index; 4) claims on the central government; 5) bank secrecy; 6) Global Terrorism Index; 7) gross domestic product per capita. The constructed models of neural networks are represented by architecture (the number of layers and hidden neurons), performance and error (training, control, test), learning algorithm, as well as error functions, active hidden and active output neurons. The reliability of the presented models is based on the following criteria: the criteria given in the columns “Training Performance”, “Control Performance”, “Test Performance”. The risk of using financial intermediaries for money laundering for the period 2019 - 2023 has been predicted, showing its gradual growth since 2020. It is proved that the predicted risk values of using financial intermediaries for money laundering, regardless of the rather low predicted level for 2019, tend to increase rapidly in the near term.