Abstract
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
Highlights
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
Summary
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. Проведен сравнительный анализ результатов обработки данных о деятельности кредитных организаций методами классификации — логистической регрессии, деревьев решений. 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. Полученных при применении различных современных алгоритмов классификации и поиска аномалий. Что алгоритм поиска аномалий на основе метода главных компонент показал более точные результаты по сравнению с алгоритмом одноклассовой машины опорных векторов. М. Сравнительный анализ методов машинного обучения при идентификации признаков вовлеченности кредитных организаций и их клиентов в сомнительные операции.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
