financeФинансы: теория и практика/Finance: Theory and PracticeFinance: Theory and Practice2587-56712587-7089Financial University under The Government of Russian Federation10.26794/2587-5671-2020-25-5-186-199finance-1329Research ArticleФИНАНСОВЫЙ МОНИТОРИНГFINANCIAL MONITORINGСравнительный анализ методов машинного обучения при идентификации признаков вовлеченности кредитных организаций и их клиентов в сомнительные операцииComparative Analysis of Machine learning Methods to Identify signs of suspicious Transactions of Credit Institutions and Their Clientshttps://orcid.org/0000-0002-1005-6265БекетноваЮ. М.BeketnovaYu. M.

Юлия Михайловна Бекетнова — кандидат технических наук, доцент Департамента информационной безопасности

Москва

Yuliya M. Beketnova — Cand. Sci. (Eng.), Assoc. Prof., Information Security Department

Moscow

beketnova@mail.ruФинансовый университетРоссияFinancial UniversityRussian Federation202128102021255186199Copyright © Бекетнова Ю.М., 20212021Бекетнова Ю.М.Beketnova Y.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://financetp.fa.ru/jour/article/view/1329

В сфере финансового мониторинга для принятия эффективных управленческих решений необходимо оперативно получать объективные оценки хозяйствующих субъектов (в частности, кредитных организаций). Автоматизация процесса выявления недобросовестных кредитных организаций на основе методов машинного обучения позволит контролирующим органам оперативно выявлять и пресекать противоправную деятельность. Цель исследования состоит в обосновании возможностей применения методов и алгоритмов машинного обучения для автоматической идентификации недобросовестных кредитных организаций. Для этого необходимо подобрать математический инструментарий анализа данных о кредитных организациях, позволяющий проводить диагностику вовлеченности банка в процессы отмывания преступных доходов. Проведен сравнительный анализ результатов обработки данных о деятельности кредитных организаций методами классификации — логистической регрессии, деревьев решений. Использован метод опорных векторов, нейросетевые методы, Байесовские сети (двухклассовая сеть Байеса) и поиска аномалий — алгоритм одноклассовой машины опорных векторов и алгоритм обнаружения аномалий на основе метода главных компонент. Приведены результаты решения задачи классификации кредитных организаций с точки зрения возможной вовлеченности в процессы отмывания денежных средств, результаты анализа данных о деятельности кредитных организаций методами выявления аномалий. Проведен сравнительный анализ результатов, полученных при применении различных современных алгоритмов классификации и поиска аномалий. Сделан вывод о том, что алгоритм поиска аномалий на основе метода главных компонент показал более точные результаты по сравнению с алгоритмом одноклассовой машины опорных векторов. Из рассмотренных алгоритмов классификации наиболее точные результаты показал алгоритм двухклассового усиленного дерева решений (Adaboost). Приведенные результаты исследования могут быть использованы Банком России и Росфинмониторингом для автоматизации выявления недобросовестных кредитных организаций.

In the field of financial monitoring, it is necessary to promptly obtain objective assessments of economic entities (in particular, credit institutions) for effective decision-making. Automation of the process of identifying unscrupulous credit institutions based on machine learning methods will allow regulatory authorities to quickly identify and suppress illegal activities. The aim of the research is to substantiate the possibilities of using machine learning methods and algorithms for the automatic identification of unscrupulous credit institutions. It is required to select a mathematical toolkit for analyzing data on credit institutions, which allows tracking the involvement of a bank in money laundering processes. The paper provides a comparative analysis of the results of processing data on the activities of credit institutions using classification methods — logistic regression, decision trees. The author applies support vector machine and neural network methods, Bayesian networks (Two-Class Bayes Point Machine), and anomaly search — an algorithm of a One-Class Support Vector Machine and a PCA-Based Anomaly Detection algorithm. The study presents the results of solving the problem of classifying credit institutions in terms of possible involvement in money laundering processes, the results of analyzing data on the activities of credit institutions by methods of detecting anomalies. A comparative analysis of the results obtained using various modern algorithms for the classification and search for anomalies is carried out. The author concluded that the PCA-Based Anomaly Detection algorithm showed more accurate results compared to the One-Class Support Vector Machine algorithm. Of the considered classification algorithms, the most accurate results were shown by the Two-Class Boosted Decision Tree (AdaBoost) algorithm. The research results can be used by the Bank of Russia and Rosfinmonitoring to automate the identification of unscrupulous credit institutions

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The authors declare that there are no conflicts of interest present.