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		<title>Assef et al 2019a - Revision history</title>
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		<updated>2026-06-10T18:34:48Z</updated>
		<subtitle>Revision history for this page on the wiki</subtitle>
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		<id>https://www.scipedia.com/wd/index.php?title=Assef_et_al_2019a&amp;diff=237647&amp;oldid=prev</id>
		<title>David.franco: David.franco moved page Draft Franco 961617659 to Assef et al 2019a</title>
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				<updated>2022-03-20T15:55:06Z</updated>
		
		<summary type="html">&lt;p&gt;David.franco moved page &lt;a href=&quot;/public/Draft_Franco_961617659&quot; class=&quot;mw-redirect&quot; title=&quot;Draft Franco 961617659&quot;&gt;Draft Franco 961617659&lt;/a&gt; to &lt;a href=&quot;/public/Assef_et_al_2019a&quot; title=&quot;Assef et al 2019a&quot;&gt;Assef et al 2019a&lt;/a&gt;&lt;/p&gt;
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				&lt;td colspan='1' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan='1' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;Revision as of 15:55, 20 March 2022&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan='2' style='text-align: center;' lang='en'&gt;&lt;div class=&quot;mw-diff-empty&quot;&gt;(No difference)&lt;/div&gt;
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		<author><name>David.franco</name></author>	</entry>

	<entry>
		<id>https://www.scipedia.com/wd/index.php?title=Assef_et_al_2019a&amp;diff=237646&amp;oldid=prev</id>
		<title>David.franco: Created page with &quot; == Abstract ==  This research aims at analyzing bank credit of legal entity (in non-default, default and temporarily default), for the purpose of assisting the decision made...&quot;</title>
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				<updated>2022-03-20T15:55:04Z</updated>
		
		<summary type="html">&lt;p&gt;Created page with &amp;quot; == Abstract ==  This research aims at analyzing bank credit of legal entity (in non-default, default and temporarily default), for the purpose of assisting the decision made...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&lt;br /&gt;
== Abstract ==&lt;br /&gt;
&lt;br /&gt;
This research aims at analyzing bank credit of legal entity (in non-default, default and temporarily default), for the purpose of assisting the decision made by the analyst of this area. For that, we used Artificial Neural Networks (ANNs), more specifically, the Multilayer Perceptron (MLP) and the Radial Basis Functions (RBF) and, also, the statistical model of Logistic Regression (LR). For the implementation of the ANNs and LR, the softwares MATLAB and SPSS were used, respectively. For the simulations developed 5.432 data with 15 attributes were collected by the experts of the institution bank (called “XYZ”). The results show that the default clients are easily identifiable, but for the nondelinquent clients and for the temporarily defaulters, the techniques had greater difficulty in the discrimination, suggesting that they are no so discriminants. The main contributions of this work are: the analysis of three classes of clients (non-default, default and temporarily default), rather than just two (non-default and default) as is usually done; the coding of variables (attributes) of the company XYZ aiming to maximize the accuracy of the techniques and the use of the one-against all method, little used by the researchers of this research area. This work presents new insights towards research over Credit Risk Assessment showing other possibilities of client classification and codification, allowing different types of studies to take place.&lt;br /&gt;
&lt;br /&gt;
== Full document ==&lt;br /&gt;
&amp;lt;pdf&amp;gt;Media:Draft_Franco_961617659-2107-document.pdf&amp;lt;/pdf&amp;gt;&lt;/div&gt;</summary>
		<author><name>David.franco</name></author>	</entry>

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