title: ÐÐ¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð²Ð»Ð¸ÑÐ½Ð¸Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÐ°ÐºÑÐ¾ÑÐ¾Ð² Ð½Ð° ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ ÑÑÐ½ÐºÑÐ¸Ð¾Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÐºÐ¾Ð¼Ð¼ÐµÑÑÐµÑÐºÐ¸Ñ Ð±Ð°Ð½ÐºÐ¾Ð² Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Modeling the influence of various factors on the efficiency of commercial banks using machine learning methods
Creators: name: Ð¡Ð°ÑÐ³ÑÑÐ½, ÐÐ°ÑÑÐµÐ»
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ
ÐºÐ¾Ð¼Ð¼ÐµÑÑÐµÑÐºÐ¸Ðµ Ð±Ð°Ð½ÐºÐ¸
ÑÐ°ÐºÑÐ¾ÑÑ
Ð¼ÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
efficiency
commercial banks
efficiency factors
machine learning methods
modeling

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description: The aim of the work is to study the factors influencing the effectiveness of commercial banks using machine learning methods. In the course of the work, the following tasks were solved: - a systematic literary review was conducted, a scientific gap was identified, and scientific hypotheses were formed; - an analytical review of methods and models for evaluating the effectiveness of commercial banks is presented; - in accordance with the stated hypotheses, a dataset was formed and a primary data analysis was performed; - modeling of the influence of various factors on the efficiency of commercial banks has been carried out; - the data obtained have been interpreted and recommendations have been developed for the application of the simulation results. The main hypothesis of the presented study is to determine the influence of various determinants on the efficiency of commercial banks based on open data. During the research, data collection was carried out using parsing tools and automated Python tools. The systematization of the efficiency factors of commercial banks was carried out using the MS Office (Excel) package. The construction of machine learning models using various libraries was carried out in an environment for the development and execution of program code in the Google Colab cloud. The main results of the study are the identification of the statistical significance of a number of factors such as the effective deposit and credit policy of the bank, capital adequacy on the efficiency of banks, as well as the multidirectional influence of the coefficients of rapid and current liquidity.