Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

• Published in: European journal of operational research Vol. 247; no. 1; pp. 124 - 136
• Main Authors: Lessmann, Stefan, Baesens, Bart, Seow, Hsin-Vonn, Thomas, Lyn C.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.11.2015; Elsevier Sequoia S.A
• Subjects: Algorithms; Benchmarks; Classification; Credit scoring; Data mining; Forecasting benchmark; OR in banking; Studies; OR in banking; Data mining; Credit scoring; Forecasting benchmark
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2015.05.030

•Large-scale benchmark of 41 classifiers across eight real-word credit scoring data sets.•Introduction of ensemble selection routines to the credit scoring community.•Analysis of six established and novel indicators to measure scorecard accuracy.•Assessment of the financial impact of different scorecards. Many years have passed since Baesens et al. published their benchmarking study of classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627–635.]. The interest in prediction methods for scorecard development is unbroken. However, there have been several advancements including novel learning methods, performance measures and techniques to reliably compare different classifiers, which the credit scoring literature does not reflect. To close these research gaps, we update the study of Baesens et al. and compare several novel classification algorithms to the state-of-the-art in credit scoring. In addition, we examine the extent to which the assessment of alternative scorecards differs across established and novel indicators of predictive accuracy. Finally, we explore whether more accurate classifiers are managerial meaningful. Our study provides valuable insight for professionals and academics in credit scoring. It helps practitioners to stay abreast of technical advancements in predictive modeling. From an academic point of view, the study provides an independent assessment of recent scoring methods and offers a new baseline to which future approaches can be compared. [Display omitted]