HELOC Applicant Risk Performance Evaluation by Topological Hierarchical Decomposition
This addresses the need for interpretable AI in finance, where regulations require explainable decisions, though it is incremental as it adapts existing topological methods like MAPPER for a specific domain.
The paper tackles the problem of explaining machine learning decisions in regulated financial contexts by proposing topological hierarchical decomposition (THD), an unsupervised/semi-supervised technique that groups data into simplicial complexes to approximate dataset topology, applied to a HELOC dataset to identify high-risk loan applicants and generate explanations for decisions.
Strong regulations in the financial industry mean that any decisions based on machine learning need to be explained. This precludes the use of powerful supervised techniques such as neural networks. In this study we propose a new unsupervised and semi-supervised technique known as the topological hierarchical decomposition (THD). This process breaks a dataset down into ever smaller groups, where groups are associated with a simplicial complex that approximate the underlying topology of a dataset. We apply THD to the FICO machine learning challenge dataset, consisting of anonymized home equity loan applications using the MAPPER algorithm to build simplicial complexes. We identify different groups of individuals unable to pay back loans, and illustrate how the distribution of feature values in a simplicial complex can be used to explain the decision to grant or deny a loan by extracting illustrative explanations from two THDs on the dataset.