title: ÐÐµÑÐ¾Ð´Ñ Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÐºÑÐµÐ´Ð¸ÑÐ½ÑÑ ÑÐ¸ÑÐºÐ¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Methods and models of machine learning for credit risk assessment
Creators: name: Ð¡Ð°Ð±ÑÑÐ¾Ð², Ð¡ÐµÑÐ³ÐµÐ¹
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: 2025
Subjects: ÐºÑÐµÐ´Ð¸ÑÐ½ÑÐ¹ ÑÐºÐ¾ÑÐ¸Ð½Ð³
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð»Ð¾Ð³Ð¸ÑÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ñ
ÑÐ»ÑÑÐ°Ð¹Ð½ÑÐ¹ Ð»ÐµÑ
Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³
xgboost
credit scoring
machine learning
logistic regression
random forest
gradient boosting
xgboost.

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÐµÑÐ¾Ð´Ñ Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÐºÑÐµÐ´Ð¸ÑÐ½ÑÑ ÑÐ¸ÑÐºÐ¾Ð²Â». ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð¸ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð¼Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð·Ð°Ð´Ð°Ñ ÐºÑÐµÐ´Ð¸ÑÐ½Ð¾Ð³Ð¾ ÑÐºÐ¾ÑÐ¸Ð½Ð³Ð°. ÐÑÐ¿Ð¾Ð»Ð½ÐµÐ½ Ð¾Ð±Ð·Ð¾Ñ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ð¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÐ¼ÑÑ Ð² Ð´Ð°Ð½Ð½Ð¾Ð¹ Ð¾Ð±Ð»Ð°ÑÑÐ¸, ÑÑÐ¾ Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ð»Ð¾ Ð¾Ð±Ð¾ÑÐ½Ð¾Ð²Ð°ÑÑ Ð¼ÐµÑÐ¾Ð´Ñ Ð´Ð»Ñ Ð¸Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð² ÑÐ°Ð±Ð¾ÑÐµ. ÐÐ°ÑÐµÐ¼ Ð±ÑÐ»Ð° Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ð¿ÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð´Ð°Ð½Ð½ÑÑ, Ð²ÐºÐ»ÑÑÐ°Ñ ÑÑÑÑÐ°Ð½ÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾Ð¿ÑÑÐºÐ¾Ð², Ð¼Ð°ÑÑÑÐ°Ð±Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð¸ ÐºÐ¾Ð´Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÐºÐ°ÑÐµÐ³Ð¾ÑÐ¸Ð°Ð»ÑÐ½ÑÑ Ð´Ð°Ð½Ð½ÑÑ, ÑÑÐ¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐ¸Ð»Ð¾ ÐºÐ¾ÑÑÐµÐºÑÐ½Ð¾Ðµ ÑÑÐ½ÐºÑÐ¸Ð¾Ð½Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð². ÐÐ° ÑÐ»ÐµÐ´ÑÑÑÐµÐ¼ ÑÑÐ°Ð¿Ðµ Ð±ÑÐ»Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, ÑÐ°ÐºÐ¸Ðµ ÐºÐ°Ðº Ð»Ð¾Ð³Ð¸ÑÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ñ, ÑÐ»ÑÑÐ°Ð¹Ð½ÑÐ¹ Ð»ÐµÑ, Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³ Ð¸ XGBoost. ÐÐ»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¹ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»Ð°ÑÑ Ð½Ð°ÑÑÑÐ¾Ð¹ÐºÐ° Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð², Ð° Ð·Ð°ÑÐµÐ¼ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°Ð»Ð¸ÑÑ Ð¿Ð¾ Ð¼ÐµÑÑÐ¸ÐºÐµ ROC-AUC, ÑÑÐ¾Ð±Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»Ð¸ÑÑ Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ. ÐÐ°Ð²ÐµÑÑÐ°ÑÑÐ¸Ð¼ ÑÑÐ°Ð¿Ð¾Ð¼ ÑÑÐ°Ð» Ð°Ð½Ð°Ð»Ð¸Ð· Ð²Ð°Ð¶Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð², ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ð» Ð¸Ð½ÑÐµÑÐ¿ÑÐµÑÐ¸ÑÐ¾Ð²Ð°ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¸ Ð¿Ð¾Ð´Ð³Ð¾ÑÐ¾Ð²Ð¸ÑÑ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¸ Ð´Ð»Ñ Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹. Ð Ð°Ð±Ð¾ÑÐ° Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Python Ð¸ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐº Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ: Pandas, Scikit-learn, Matplotlib, XGBoost. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð±ÑÐ»Ð¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ ÑÐ¾ÑÐ½Ð¾Ð¹ Ð¸Ð· ÐºÐ¾ÑÐ¾ÑÑÑ Ð¾ÐºÐ°Ð·Ð°Ð»Ð°ÑÑ XGBoost, Ð¿Ð¾ÐºÐ°Ð·Ð°Ð²ÑÐ°Ñ Ð½Ð°Ð¸Ð²ÑÑÑÐµÐµ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ðµ ROC-AUC. ÐÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¸ Ð´Ð»Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð² ÑÐµÐ°Ð»ÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼Ð°Ñ ÐºÑÐµÐ´Ð¸ÑÐ½Ð¾Ð³Ð¾ ÑÐºÐ¾ÑÐ¸Ð½Ð³Ð°.
description: The subject of the graduate qualification work is âMethods and models of machine learning for credit risk assessmentâ. This study focuses on the research of methods and models of machine learning used for credit risk assessment. An overview of the existing approaches and models of machine learning used in this field was conducted to identify the most suitable approaches. Data preprocessing followed, including handling missing values, scaling features, and encoding categorical variables, ensuring the datasets were optimized for model training. Machine learning models such as logistic regression, random forest, gradient boosting, and XGBoost were then developed and tested. To enhance model performance, hyperparameter tuning was performed, and models were compared using the ROC-AUC metric to evaluate their effectiveness. Finally, feature importance was analyzed to gain insights into the key factors influencing predictions, and recommendations were provided to further improve the accuracy and reliability of credit risk assessments. The work was performed using Python and modern data analysis and machine learning libraries such as Pandas, Scikit-learn, Matplotlib, and XGBoost. The study concluded that the XGBoost model demonstrated the highest accuracy among the considered algorithms. Based on the results, practical recommendations were developed for applying machine learning methods in credit scoring systems.