Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/328488 
Year of Publication: 
2022
Citation: 
[Journal:] Economies [ISSN:] 2227-7099 [Volume:] 10 [Issue:] 8 [Article No.:] 188 [Year:] 2022 [Pages:] 1-21
Publisher: 
MDPI, Basel
Abstract: 
COVID-19 has caused an economic crisis in the business world, leaving limitations in the continuity of the payment chain, with companies resorting to credit access. This study aimed to determine the optimal machine learning predictive model for the credit risk of companies under the Reactiva Peru Program because of COVID-19. A multivariate regression analysis was applied with four regressor variables (economic sector, granting entity, amount covered, and department) and one predictor (risk level), with a population of 501,298 companies benefiting from the program, under the CRISP-DM methodology oriented especially for data mining projects, with artificial intelligence techniques under the machine learning Lasso and Ridge regression models, with econometric algebraic mathematical verification to compare and validate the predictive models using SPSS, Jamovi, R Studio, and MATLAB software. The results revealed a better Lasso regression model (λ60 = 0.00038; RMSE = 0.3573685) that optimally predicted the level of risk compared to the Ridge regression model (λ100 = 0.00910; RMSE = 0.3573812) and the least squares model with algebraic mathematics, which corroborates that the Lasso regression model is the best predictive model to detect the level of credit risk of the Reactiva Peru Program. The best predictive model for detecting the level of corporate credit risk is the Lasso regression model.
Subjects: 
machine learning
credit risk
credits
Lasso model
Ridge model
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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