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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Bibliographic Details
Published in:Economies Vol. 10; no. 8; pp. 1 - 21
Main Authors: Geraldo-Campos, Luis Alberto, Soria, Juan J, Pando-Ezcurra, Tamara
Format: Journal Article
Language:English
Published: Basel MDPI 01.07.2022
MDPI AG
Subjects:
Banks
Beneficiaries
Comparative analysis
Coronaviruses
COVID-19
Credit risk
credits
Economic aspects
Economic crisis
Epidemics
Financial risk
Lasso model
Loans
Machine learning
Measuring instruments
Pandemics
Peru
Quality control
Regression analysis
Ridge model
Stockholders
Swaps (Finance)
Working capital
Peru
ISSN:2227-7099, 2227-7099
Online Access:Get full text
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Summary: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.
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ISSN:2227-7099
2227-7099
DOI:10.3390/economies10080188