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https://hdl.handle.net/1822/52136
Título: | An automated literature analysis on data mining applications to credit risk assessment |
Autor(es): | Moro, Sérgio Cortez, Paulo Rita, Paulo |
Data: | 2016 |
Editora: | Palgrave Macmillan |
Citação: | In C.L. Dunis, P. Middleton, T. Konstantinos and A. Karathanasopoulos (Eds.), Artificial Intelligence in Financial Markets: Cutting Edge Applications for Risk Management, Portfolio Optimization and Economics book, series: New Developments in Quantitative Trading and Investment, pp. 161-177, November, 2016, Palgrave Macmillan, ISBN 978-1-137-48879-4. |
Resumo(s): | [Extract] Introduction Financial crises are catalysts to an outstanding increase in research that will develop innovative techniques that anticipate fnancial crises thus providing a major advantage and the opportunity to take preventative action [1]. Terefore, research in fnancial credit risk has become one of the most prominent and prolifc subjects in recent years. In fact, the 2008 global fnancial crisis arose due to a poor assessment of the risk associated with the various ways through which banks have transferred credit risk in the fnancial system [2]. Tis then spread worldwide highlighting a global systemic risk... |
Tipo: | Capítulo de livro |
URI: | https://hdl.handle.net/1822/52136 |
ISBN: | 978-1-137-48879-4 |
DOI: | 10.1057/978-1-137-48880-0_6 |
Versão da editora: | The original publication is available at http://link.springer.com/chapter/10.1057/978-1-137-48880-0_6 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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Ficheiro | Descrição | Tamanho | Formato | |
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2016_BookChapter-MoroCortezRita.pdf Acesso restrito! | 1,7 MB | Adobe PDF | Ver/Abrir |