Топ-10 конкурентов volsu.ru

vsu.edu

This section provides information about VSU's radio station

manhattan.edu

Manhattan is where your passions find purpose. With experience-driven degrees and hands-on learning at every step.

admissions.duke.edu

How to apply for admissions and financial aid at Duke University

ba.hse.ru

Узнайте, вошли ли вы в приказ о зачислении в Высшую школу экономики. Это быстро и для этого понадобится только Уникальный идентификатор поступающего

pnw.edu

Financial Literacy is understanding how money works and how to make informed and effective choices with your money.

hub.edu.vn

trường đh ngân hàng thành phố hồ chí minh định hướng trở thành đại học đa ngành và liên ngành nằm trong nhóm các đại học hàng đầu khu vực và châu lục trong lĩnh vực kinh tế, kinh doanh, quản lý, pháp luật, xã hội và nhân văn.

narxoz.kz

Официальный сайт Университета Нархоз — ведущего бизнес-университета Центральной Азии

cyberleninka.ru

The banking system is a key sector of the economy and often faces various types of risks. This article focuses on credit risks, which are the main source of financial losses for credit institutions, including banks. Based on real data, a scoring model was developed in this study to assess and optimally manage credit risk, using the XGBoost machine learning algorithm. The model returns score values in the range of 300–850. Quality metrics such as ROC AUC, PR AUC, Precision, Recall, and F1 score were evaluated for the developed model. Within the scope of the research, a new approach was proposed to assess the sensitivity of a machine learning model to changes in variable values. The approach is based on the concept of entropy. The results show that the use of machine learning models makes it possible to assess and optimally manage credit risk, thereby reducing potential losses. Moreover, the proposed new approach, when applied together with the well-known SHAP method, allows for a deeper evaluation of the model and its sensitivity to changes in variable values.