Portfolio recommendations to improve risk of default in microfinance

Main Article Content

Irving Simonin http://orcid.org/0000-0002-6298-2359
Marc Brooks http://orcid.org/0000-0002-4219-6422
Luis Enrique Nieto Barajas


This article presents an exciting application of machine learning for loan origination in microfinance. Microfinance targets people who cannot build a credit history and therefore cannot access loans from banks or other financial institutions. We use data from a Mexican microfinance company that operates in several regions throughout the country. The objective is to guide intermediate lenders to choose their clients and achieve a lowerr credit default risk. We use several statistical models such as principal component analysis, clustering analysis and a regression tree. We obtain, as a result, a series of recommendations based on the characteristics of the clients. 

Article Details

Como citar
SIMONIN, Irving; BROOKS, Marc; NIETO BARAJAS, Luis Enrique. Portfolio recommendations to improve risk of default in microfinance. CIENCIA ergo-sum, [S.l.], v. 28, n. 1, dic. 2020. ISSN 2395-8782. Disponible en: <https://cienciaergosum.uaemex.mx/article/view/13175>. Fecha de acceso: 20 mayo 2022 doi: https://doi.org/10.30878/ces.v28n1a6.
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