Abstract
Computational Topological Data Analysis (TDA) is a collection of procedures which permits extracting certain robust features of high dimensional data, even when the number of data points is relatively small. Classical statistical data analysis is not very successful at or even cannot handle such situations altogether. Hidden features or structure in high dimensional data expresses some direct and indirect links between data points. Such may be the case when there are no explicit links between persons like clients in a database but there may still be important implicit links which characterize client populations and which also make different such populations more comparable. We explore the potential usefulness of applying TDA to different versions of credit scoring data, where clients are credit takers with a known defaulting behavior.
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Schebesch, K.B., Stecking, R.W. (2017). Topological Data Analysis for Extracting Hidden Features of Client Data. In: Dörner, K., Ljubic, I., Pflug, G., Tragler, G. (eds) Operations Research Proceedings 2015. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-42902-1_65
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DOI: https://doi.org/10.1007/978-3-319-42902-1_65
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