Explaining risks: axiomatic risk attributions for financial models

Dangxing Chen

Quantitative Finance, 2025, vol. 25, issue 6, 1007-1014

Abstract: In recent years, machine learning models have achieved great success at the expense of highly complex black-box structures. By using axiomatic attribution methods, we can fairly allocate the contributions of each feature, thus allowing us to interpret the model predictions. In high-risk sectors such as finance, risk is just as important as mean predictions. Throughout this work, we address the following risk attribution problem: how to fairly allocate the risk given a model with data? We demonstrate with analysis and empirical examples that risk can be well allocated by extending the Shapley value framework.

Date: 2025
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DOI: 10.1080/14697688.2025.2519837

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