Portfolio recommendations to improve risk of default in microfinance - Simonin, Irving; Brooks, Marc; Nieto Barajas, Luis Enrique (CIENCIA ergo-sum; Vol. 28 Núm. 1 (2021): CIENCIA ergo-sum (marzo-junio 2021)DO - 10.30878/ces.v28n1a6, 2020)
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.