FORECASTING EXPECTED BANK LOSSES AT GRANTING A LOAN
DOI:
https://doi.org/10.54309/IJICT.2021.05.1.019Keywords:
component, data analysis, credit risk, Loss Given Default, Expected Loss, Probability of Default, Exposure at Default, logistic regression, model, non-performing loans, Special Air Service platform, forecastingAbstract
This article uses the sample data of the SAS platform as an example to introduce the statisti-cal analysis and prediction of the expected loss of loans issued by banks. The original data for this study comes from a Kaggle source, which provides information about the credit history of bank customers. The technology is based on logistic regression, graphical data analysis, and the basis of building a model on the SAS platform. The model can be used to predict credit risk and describe credit risk in the banking system.
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Copyright (c) 2021 International Journal of Information and Communication Technologies
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
https://creativecommons.org/licenses/by-nc-nd/3.0/deed.en