Citas bibligráficas
Hernández, C., Quispe, L. (2014). Sistema de Credit Scoring para minimizar el riesgo crediticio en la cartera pyme de la Cooperativa de Ahorro y Crédito León XIII [Tesis, Universidad Privada del Norte]. https://hdl.handle.net/11537/1357
Hernández, C., Quispe, L. Sistema de Credit Scoring para minimizar el riesgo crediticio en la cartera pyme de la Cooperativa de Ahorro y Crédito León XIII [Tesis]. PE: Universidad Privada del Norte; 2014. https://hdl.handle.net/11537/1357
@misc{renati/520157,
title = "Sistema de Credit Scoring para minimizar el riesgo crediticio en la cartera pyme de la Cooperativa de Ahorro y Crédito León XIII",
author = "Quispe Becerril, Luis Fernando",
publisher = "Universidad Privada del Norte",
year = "2014"
}
The saving and Credit’s Cooperatives are exposed to a serie of existing risks that can affect seriously the development of its. From all the risks that can have the cooperatives, the Creditice Risk is in ocurrency the one that is shown more frecuently, and it’s maybe the most variated and complex, its incidence can cause a great impact in the economic situation of the institution , explanation that is maintained in the fact that the credit represents one of the fundamental activities of the cooperatives. Facing this, and with the propose to reduce the risk levels, it was necessary to develop innovating actions to the area, with the finality to be able to evaluate the expected looses in front of the non-fulfillment of payment of the customer’s part. In our study case, the saving and Credit’s Cooperative “Leon XIII” is not free about this risk, that is it’s main worryness, because the defaulting index in their clients’ portfolio has increased in the last years. The year 2008 the index is about 3.69%, the year 2009 the index is about 6.41% and in the year 2010 the index is about 8.51% In order to mitigate and reduce the creditician risk, our project will focus in the implementation of a Credit Scoring that is an automatic evaluation system, faster, safer and consistent to determine the consentions of credits, which, in function of all the available information, it’s able to predict the probability of non-payment, associated to a creditician operation. This system will allow reducing the defaulting levels in the pyme clients’ portfolio, reducing in this way the evaluation time, letting us a better evaluation and support of credits, achieving it to be an important tool for credit’s analists.
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