Explaining Risks: Axiomatic Risk Attributions for Financial Models

Dangxing Chen

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arXiv · 2506.06653

Explaining Risks: Axiomatic Risk Attributions for Financial Models

Dangxing Chen

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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.

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q-fin.CP cs.LG stat.ML

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Dangxing Chen. 2025-06-07. Explaining Risks: Axiomatic Risk Attributions for Financial Models. https://arxiv.org/abs/2506.06653

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