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The Most Relevant Variables To Support Risk Analysts For Loan Decisions: An Empirical Study

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  • M. José Charlo
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    Abstract

    This paper presents an empirical study about risk analysis using several technologies. We have investigated two fundamental questions: we have analized the results obtained using each specific technology (discriminant analysis, logistic regression, and an artificial intelligence technology) and we have pointed out the most relevant variables for this kind of decision making.The percentage of error when using an artificial intelligence technology allows us to conclude that these intelligent systems are a good support in decision-making for risk analysts from banking entities.The most important variable using an artificial intelligence technology is the firm’s economic situation, followed by firm rating and by firm image.

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    File URL: http://www.usc.es/economet/journals2/eers/eers1014.pdf
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    Bibliographic Info

    Article provided by Euro-American Association of Economic Development in its journal Regional and Sectoral Economic Studies.

    Volume (Year): 10 (2010)
    Issue (Month): 1 ()
    Pages:

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    Handle: RePEc:eaa:eerese:v:10:y2010:i:10_4

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    Related research

    Keywords: Credit risk analysis; artificial intelligence; CBR; discriminant analysis; logistic regression.;

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    1. Cecilio Mar-Molinero & Carlos Serrano-Cinca, 2001. "Bank failure: a multidimensional scaling approach," The European Journal of Finance, Taylor & Francis Journals, vol. 7(2), pages 165-183.
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