2024/08/11 by Dangxing Chen, Chen, Dangxing, J. H. Chen +3 · 1 citation
Arts and Humanities · #Computational Finance (q-fin.CP) #FOS: Economics and business #Philosophy and History of Science #Philosophy, Science, and History
paper · pdf · doi:10.48550/arxiv.2408.05701
openalex publication_date 2024/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Explainable machine learning methods have been accompanied by substantial development. Despite their success, the existing approaches focus more on the general framework with no prior domain expertise. High-stakes financial sectors have extensive domain knowledge of the features. Hence, it is expected that explanations of models will be consistent with domain knowledge to ensure conceptual soundness. In this work, we study the group structures of features that are naturally formed in the financial dataset. Our study shows the importance of considering group structures that conform to the regulations. When group structures are present, direct applications of explainable machine learning methods, such as Shapley values and Integrated Gradients, may not provide consistent explanations; alternatively, group versions of the Shapley value can provide consistent explanations. We contain detailed examples to concentrate on the practical perspective of our framework.