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Classification algorithms are used in many domains to extract information from data, predict the entry probability of events of interest, and, eventually, support decision making. This paper explores the potential of extreme learning machines (ELM), a recently proposed type of artificial neural network, for consumer credit risk management. ELM possess some interesting properties, which might enable them to improve the quality of model-based decision support. To test this, we empirically compare ELM to established scoring techniques according to three performance criteria: ease of use, resource consumption, and predictive accuracy. The mathematical roots of ELM suggest that they are especially suitable as a base model within ensemble classifiers. Therefore, to obtain a holistic picture of their potential, we assess ELM in isolation and in conjunction with different ensemble frameworks. The empirical results confirm the conceptual advantages of ELM and indicate that they are a valuable alternative to other credit risk modelling methods. [ABSTRACT FROM AUTHOR]

Financial institutions use credit scorecards for risk management. A scorecard is a data-driven model for predicting default probabilities. Scorecard assessment concentrates on how well a scorecard discriminates good and bad risk. Whether predicted and observed default probabilities agree (i.e., calibration) is an equally important yet often overlooked dimension of scorecard performance. Surprisingly, no attempt has been made to systematically explore different calibration methods and their implications in credit scoring. The goal of the paper is to integrate previous work on probability calibration, to re-introduce available calibration techniques to the credit scoring community, and to empirically examine the extent to which they improve scorecards. More specifically, using real-world credit scoring data, we first develop scorecards using different classifiers, next apply calibration methods to the classifier predictions, and then measure the degree to which they improve calibration. To evaluate performance, we measure the accuracy of predictions in terms of the Brier Score before and after calibration, and employ repeated measures analysis of variance to test for significant differences between group means. Furthermore, we check calibration using reliability plots and decompose the Brier Score to clarify the origin of performance differences across calibrators. The observed results suggest that post-processing scorecard predictions using a calibrator is beneficial. Calibrators improve scorecard calibration while the discriminatory ability remains unaffected. Generalized additive models are particularly suitable for calibrating classifier predictions. [ABSTRACT FROM AUTHOR]