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Strong regulations in the financial industry mean that any decisions based onmachine learning need to be explained. This precludes the use of powerfulsupervised techniques such as neural networks. In this study we propose a newunsupervised and semi-supervised technique known as the topologicalhierarchical decomposition (THD). This process breaks a dataset down into eversmaller groups, where groups are associated with a simplicial complex thatapproximate the underlying topology of a dataset. We apply THD to the FICOmachine learning challenge dataset, consisting of anonymized home equity loanapplications using the MAPPER algorithm to build simplicial complexes. Weidentify different groups of individuals unable to pay back loans, andillustrate how the distribution of feature values in a simplicial complex canbe used to explain the decision to grant or deny a loan by extractingillustrative explanations from two THDs on the dataset.

HELOC Applicant Risk Performance Evaluation by Topological Hierarchical Decomposition