Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models

Luis Alberto Geraldo-Campos, Juan J. Soria and Tamara Pando-Ezcurra
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Luis Alberto Geraldo-Campos: Dirección de Investigación, Universidad Privada Peruano Alemana, Chorrillos, Lima 15064, Peru
Juan J. Soria: Facultad de Ingeniería de Sistemas y Electrónica, Universidad Tecnológica del Perú, Villa el Salvador, Lima 15842, Peru
Tamara Pando-Ezcurra: Dirección de Investigación, Universidad Privada Peruano Alemana, Chorrillos, Lima 15064, Peru

Economies, 2022, vol. 10, issue 8, 1-21

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 ( λ 60 = 0.00038; RMSE = 0.3573685) that optimally predicted the level of risk compared to the Ridge regression model ( λ 100 = 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.

Keywords: Lasso model; Ridge model; credits; machine learning; credit risk (search for similar items in EconPapers)
JEL-codes: E F I J O Q (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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