Portfolio recommendations to improve risk of default in microfinance

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

Resumen

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: 25 sep. 2021 doi: https://doi.org/10.30878/ces.v28n1a6.
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Ciencias exactas y aplicadas

Citas

Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey, C. A.: Wadsworth & Brooks.

Breiman, L. (2001). Machine Learning, 45, 5. 10.1023/A:1010933404324

Condusef. (2014). Microcréditos, el costo de contratarlos. Proteja su dinero. Retrieved from https://www.condusef.gob.mx/Revista/index.php/credito/personal/404-microcreditos

Lara-Rubio, J. (2010). La gestión del riesgo de crédito en las instituciones de microfinanzas. Retrieved from https://hera.ugr.es/tesisugr/18892656.pdf

Lever, J., Krzywinski, M., & Altman, N. (2017). Principal component analysis. Nature Methods, 14, 641-642. https://doi.org/10.1038/nmeth.4346

Loh, W.-Y. (2008). Classification and regression tree methods. In Encyclopedia of Statistics in Quality and Reliability (pp. 315-323). Wiley.

Murtagh, F., & Legendre, P. (2014). Ward’s hierarchical agglomerative clustering method: Which algorithms implement ward’s criterion? Journal of Classification, 31, 274-295.

Schreiner, M. (2002). Scoring: The Next Breakthrough in Microcredit? Technical report. Retrieved from http://www.microfinance.com/English/Papers/Scoring_Breakthrough.pdf