000			02047naa a2200241 a 4500
003			AR-LpUFIB
005			20250311170452.0
008			230201s2017 xx o 000 0 eng d
024	8		_aDIF-M7753 _b7973 _zDIF007083
040			_aAR-LpUFIB _bspa _cAR-LpUFIB
100	1		_aLanzarini, Laura Cristina
245	1	0	_aSimplifying credit scoring rules using LVQ + PSO
300			_a1 archivo (134,1 kB)
500			_aFormato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
520			_aOne of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borrower. Processing this information can be time consuming, and presents some difficulties due to the heterogeneous structure of data. We offer in this paper an alternative method that is able to classify customers’ profiles from numerical and nominal attributes. The key feature of our method, called LVQ+PSO, is the finding of a reduced set of classifying rules. This is possible, due to the combination of a competitive neural network with an optimization technique. These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method not only useful for credit officers aiming to make quick decisions about granting a credit, but also could act as borrower’s self selection. Our method was applied to an actual database of a credit consumer financial institution in Ecuador. We obtain very satisfactory results. Future research lines are exposed.
534			_aKybernetes, 46(1), pp. 8-16.
653			_ariesgo de crédito
700	1		_aVilla Monte, Augusto
700	1		_aBariviera, Aurelio F.
700	1		_aJimbo Santana, Patricia
856	4	0	_uhttp://dx.doi.org/10.1108/K-06-2016-0158
942			_cCP
999			_c56859 _d56859