APPLICATION OF MACHINE LEARNING IN DETECTING LOAN DELINQUENCY: CASE STUDY OF MICROFINANCE INSTITUTION IN UZBEKISTAN
Аннотация
The rise of the internet has revolutionized the way we live, work, and communicate. Alongside this digital revolution, a new phenomenon has emerged - big data
Библиографические ссылки
Galindo, J., & Tamayo, P., 2000. Credit risk assessment using statistical and machine learning: Basic methodology and risk modeling applications. Computational Economics 15, 107–143.
Wang, Y., Zhang, Y., Lu, Y., & Yu, X., 2020. A Comparative Assessment of Credit Risk Model Based on Machine Learning a case study of bank loan data. Procedia Computer Science, 174, 141–149.
Xusheng L., & Yaohuang G., 2006. Personal credit evaluation model based on Naive Bayes classifier [J]. Computer Engineering and Applications, 42(30): 197-201.
Kim S.H., Oh K.J., Ju J.B., & Lee D.W., 2019. Predicting Debt Default of P2P Loan Borrowers Using Self-Organizing Map. Quantitative Bio-Science, 38(1), 63–71.
Moscato, V., Picariello, A., & Sperlí, G., 2021. A benchmark of machine learning approaches for credit score prediction. Expert Systems With Applications, 165, 113986.
Luo, S., Cheng, B., & Hsieh, C., 2009. Prediction model building with clustering-launched classification and support vector machines in credit scoring. Expert Systems with Applications, 36 (4), 7562–7566.
Yu, L., Yue, W., Wang, S., & Lai, K. K., 2010. Support vector machine based multi- agent ensemble learning for credit risk evaluation. Expert Systems with Applications, 37 (2), 1351–1360.
Загрузки
Опубликован
Как цитировать
Выпуск
Раздел
Лицензия
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.
