APPLICATION OF MACHINE LEARNING IN DETECTING LOAN DELINQUENCY: CASE STUDY OF MICROFINANCE INSTITUTION IN UZBEKISTAN

Mualliflar

DOI:

https://doi.org/10.60078/2023-vol1-iss1-pp157-160

Annotasiya

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

Bibliografik manbalar

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.

Yuklashlar

Nashr qilingan

Qanday qilib iqtibos keltirish kerak

Isakov, O. (2023). APPLICATION OF MACHINE LEARNING IN DETECTING LOAN DELINQUENCY: CASE STUDY OF MICROFINANCE INSTITUTION IN UZBEKISTAN. Nashrlar, 1(1), 157–160. https://doi.org/10.60078/2023-vol1-iss1-pp157-160