Explaining Risks: Axiomatic Risk Attributions for Financial Models

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.

@article{chen2025_2506.06653,
  title={ Explaining Risks: Axiomatic Risk Attributions for Financial Models },
  author={ Dangxing Chen },
  journal={arXiv preprint arXiv:2506.06653},
  year={ 2025 }
}