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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

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.

Suggested Citation

  • M. José Charlo, 2010. "The Most Relevant Variables To Support Risk Analysts For Loan Decisions: An Empirical Study," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 10(1).
    • Handle: RePEc:eaa:eerese:v:10:y2010:i:10_4
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    References listed on IDEAS

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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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    More about this item

    Keywords

    Credit risk analysis; artificial intelligence; CBR; discriminant analysis; logistic regression.;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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