Bibliographic citations
Alarcón, E., Mora, B. (2020). Modelo para la evaluación del riesgo crediticio para los clientes de las microfinancieras del Perú [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/650407
Alarcón, E., Mora, B. Modelo para la evaluación del riesgo crediticio para los clientes de las microfinancieras del Perú [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2020. http://hdl.handle.net/10757/650407
@misc{renati/389467,
title = "Modelo para la evaluación del riesgo crediticio para los clientes de las microfinancieras del Perú",
author = "Mora Ramos, Brian Javier",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2020"
}
The growth and importance of the microfinance institutions of Peru are increasing over the years, generating a greater grant of credits to people who use this service. Likewise, the credit risk is a problem that has been affecting this type of financial institution since its birth because there is a probability that the client does not finish paying the loan, even closing some companies. In Peru, all financial institutions use the delinquency rate as an indicator to measure the percentage of unpaid loans with respect to all placements, with double the regular banking in microfinance. This problem originates due to the lack of modern evaluation models that use objective variables. For this reason, in the present document we design a prediction model that allows to the Microfinance institutions the facility to take more accurate decisions about given a credit to the customers. We use standards, tools and modern algorithms that allow the evaluation of every variable. Likewise, this research seeks to design a prediction model that allows better decision making when evaluating the borrower, using standard and common variables for this type of institutions, and modern tools and algorithms that allow a better evaluation of the variables with respect to the information provided. The result of the investigation shows the variables and percentages of payment prediction with the information provided by a microfinance institution.
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