On preprocessing data for financial credit risk evaluation |
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| Affiliation: | 1. School of Management, China University of Mining and Technology, Xuzhou, Jiangsu 221116, PR China;2. School of Foreign Studies, China University of Mining and Technology, Xuzhou, Jiangsu 221116, PR China;1. Université de Limoges, LAPE, 5 rue Félix Eboué, 87031 Limoges Cedex, France;2. JPLC SASU, 54 avenue de la Révolution, 87000 Limoges, France;1. School of Economics and Management, University of Porto, Portugal;2. Laboratory of Artificial Intelligence and Decision Support of the Institute for Systems and Computer Engineering, Technology and Science, Portugal;1. School of Business and Economics, Humboldt-University of Berlin, Unter den Linden 6, 10099 Berlin, Germany;2. Department of Decision Sciences & Information Management, Catholic University of Leuven, Naamsestraat 69, B-3000 Leuven, Belgium;3. School of Management, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom;4. Nottingham University Business School, University of Nottingham-Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia;1. EconomiX-CNRS, University of Paris Nanterre, 200 Avenue de la République, Nanterre 92000, France;2. Univ. Orléans, CNRS, LEO (FRE 2014), Rue de Blois, Orléans 45067, France |
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| Abstract: | Financial credit-risk evaluation is among a class of problems known to be semi-structured, where not all variables that are used for decision-making are either known or captured without error. Machine learning has been successfully used for credit-evaluation decisions. However, blindly applying machine learning methods to financial credit risk evaluation data with minimal knowledge of data may not always lead to expected results. We present and evaluate some data and methodological considerations that are taken into account when using machine learning methods for these decisions. Specifically, we consider the effects of preprocessing of credit-risk evaluation data used as input for machine learning methods. |
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