COMPARATIVE ANALYSIS OF CLASSIFICATION METHODS IN PROCESSING DATA ON THE ACTIVITIES OF CREDIT ORGANIZATIONS

Abstract

The results of solving the classification problem of credit organizations from the point of view of possible involvement in the money laundering processes are presented. A comparative analysis of the results obtained using various modern classification algorithms is carried out. When analyzing credit institutions, Rosfinmonitoring analysts have to operate with large amounts of information. The actual need for the number of objects to be analyzed is in many times greater than the capabilities of analysts. This problematic situation requires prioritization of inspections. The heterogeneous nature of information resources and their significant volume exclude the possibility of their manual processing. It is necessary to move from successive expert examinations of individual objects to parallel mass automated checks, taking into account modern methodological and instrumental possibilities in the context of digital transformation of public administration. A comparative analysis of the results of processing data on the activities of credit organizations by classification methods – logistic regression, decision trees (algorithms of Two-Class Boosted Decision Forest, AdaBoost), the method of support vectors (algorithm of Two-Class Support Vector Machine), neural network methods (algorithm of Two-Class Neural Network), Bayesian networks (the algorithm of Two-Class Bayes Pointmachine) carried out. Of the classification algorithms considered, the most accurate results were shown by the algorithm of Two-Class Boosted Decision Forest (AdaBoost). The results obtained are of great practical importance and may allow Rosfinmonitoring analysts, as well as experts of the Bank of Russia, to identify deviant credit institutions potentially involved in money laundering processes.