Portfolio recommendations to improve risk of default in microfinance
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 ope...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | México |
| Institución: | Universidad Nacional Autónoma de México |
| Repositorio: | Redalyc-UNAM |
| OAI Identifier: | oai:redalyc.org:10464915004 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=10464915004 https://www.redalyc.org/journal/104/10464915004/ https://www.redalyc.org/journal/104/10464915004/html/ https://www.redalyc.org/journal/104/10464915004/10464915004.epub https://www.redalyc.org/journal/104/10464915004/movil https://doi.org/10.30878/ces.v28n1a6 |
| Access Level: | acceso abierto |
| Palabra clave: | Multidisciplinarias (Ciencias Sociales) microfinance risk of default regression tree machine learning Clustering analysis |
| Sumario: | 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. |
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