Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models
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 Pr...
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2022
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| _version_ | 1881508930147844096 |
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| author | Luis Alberto Geraldo Campos Juan J. Soria Tamara Pando-Ezcurra |
| author_browse | Juan J. Soria Luis Alberto Geraldo Campos Tamara Pando-Ezcurra |
| author_facet | Luis Alberto Geraldo Campos Juan J. Soria Tamara Pando-Ezcurra |
| author_sort | Luis Alberto Geraldo Campos |
| collection | Producción científica |
| description | 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. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/997 |
| institution | UPeU |
| language | English |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
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| spelling | oai:cris.upeu.edu.pe:20.500.15037/9972026-10-30T16:15:04ZMachine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression ModelsLuis Alberto Geraldo CamposJuan J. SoriaTamara Pando-EzcurraLasso (programming language)Credit riskElastic net regularizationRegression analysisComputer scienceMachine learningMultivariate adaptive regression splinesRegressionLogistic regressionPopulationArtificial intelligenceEconometricsStatisticsActuarial scienceNonparametric regressionMathematicsEconomicsFeature selectionMedicineEnvironmental healthWorld Wide WebLasso (programming language)Credit riskElastic net regularizationRegression analysisComputer scienceMachine learningMultivariate adaptive regression splinesRegressionLogistic regressionPopulationArtificial intelligenceEconometricsStatisticsActuarial scienceNonparametric regressionMathematicsEconomicsFeature selectionMedicineSocial Sciences Economics, Econometrics and Finance Economics and Econometrics Business, Innovation, and EconomySocial Sciences Business, Management and Accounting Accounting Business, Education, Mathematics ResearchSocial Sciences Business, Management and Accounting Accounting Financial Distress and Bankruptcy PredictionCOVID-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.Articlehttps://www.mdpi.com/2227-7099/10/8/188/pdf?version=1659582501https://doi.org/10.3390/economies1008018810.3390/economies10080188https://hdl.handle.net/20.500.15037/997W4289109823enEconomies2227-70992022-07-302026-06-13T09:42:40Z2026-06-13T09:42:40Z |
| spellingShingle | Lasso (programming language) Credit risk Elastic net regularization Regression analysis Computer science Machine learning Multivariate adaptive regression splines Regression Logistic regression Population Artificial intelligence Econometrics Statistics Actuarial science Nonparametric regression Mathematics Economics Feature selection Medicine Environmental health World Wide Web Lasso (programming language) Credit risk Elastic net regularization Regression analysis Computer science Machine learning Multivariate adaptive regression splines Regression Logistic regression Population Artificial intelligence Econometrics Statistics Actuarial science Nonparametric regression Mathematics Economics Feature selection Medicine Social Sciences Economics, Econometrics and Finance Economics and Econometrics Business, Innovation, and Economy Social Sciences Business, Management and Accounting Accounting Business, Education, Mathematics Research Social Sciences Business, Management and Accounting Accounting Financial Distress and Bankruptcy Prediction Luis Alberto Geraldo Campos Juan J. Soria Tamara Pando-Ezcurra Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models |
| title | Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models |
| title_full | Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models |
| title_fullStr | Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models |
| title_full_unstemmed | Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models |
| title_short | Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models |
| title_sort | machine learning for credit risk in the reactive peru program a comparison of the lasso and ridge regression models |
| topic | Lasso (programming language) Credit risk Elastic net regularization Regression analysis Computer science Machine learning Multivariate adaptive regression splines Regression Logistic regression Population Artificial intelligence Econometrics Statistics Actuarial science Nonparametric regression Mathematics Economics Feature selection Medicine Environmental health World Wide Web Lasso (programming language) Credit risk Elastic net regularization Regression analysis Computer science Machine learning Multivariate adaptive regression splines Regression Logistic regression Population Artificial intelligence Econometrics Statistics Actuarial science Nonparametric regression Mathematics Economics Feature selection Medicine Social Sciences Economics, Econometrics and Finance Economics and Econometrics Business, Innovation, and Economy Social Sciences Business, Management and Accounting Accounting Business, Education, Mathematics Research Social Sciences Business, Management and Accounting Accounting Financial Distress and Bankruptcy Prediction |
| topic_facet | Lasso (programming language) Credit risk Elastic net regularization Regression analysis Computer science Machine learning Multivariate adaptive regression splines Regression Logistic regression Population Artificial intelligence Econometrics Statistics Actuarial science Nonparametric regression Mathematics Economics Feature selection Medicine Environmental health World Wide Web Lasso (programming language) Credit risk Elastic net regularization Regression analysis Computer science Machine learning Multivariate adaptive regression splines Regression Logistic regression Population Artificial intelligence Econometrics Statistics Actuarial science Nonparametric regression Mathematics Economics Feature selection Medicine Social Sciences Economics, Econometrics and Finance Economics and Econometrics Business, Innovation, and Economy Social Sciences Business, Management and Accounting Accounting Business, Education, Mathematics Research Social Sciences Business, Management and Accounting Accounting Financial Distress and Bankruptcy Prediction |
| url | https://www.mdpi.com/2227-7099/10/8/188/pdf?version=1659582501 https://doi.org/10.3390/economies10080188 https://hdl.handle.net/20.500.15037/997 |
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