On preprocessing data for financial credit risk evaluation

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.