Explainable Risk Classification in Financial Reports
<|reference_start|>Explainable Risk Classification in Financial Reports: Every publicly traded company in the US is required to file an annual 10-K financial report, which contains a wealth of information about the company. In this paper, we propose an explainable deep-learning model, called FinBERT-XRC, that takes a 10-K report as input, and automatically assesses the post-event return volatility risk of its associated company. In contrast to previous systems, our proposed model simultaneously offers explanations of its classification decision at three different levels: the word, sentence, and corpus levels. By doing so, our model provides a comprehensive interpretation of its prediction to end users. This is particularly important in financial domains, where the transparency and accountability of algorithmic predictions play a vital role in their application to decision-making processes. Aside from its novel interpretability, our model surpasses the state of the art in predictive accuracy in experiments on a large real-world dataset of 10-K reports spanning six years.<|reference_end|>
@article{tan2024explainable,
    title={Explainable Risk Classification in Financial Reports},
    author={Xue Wen Tan, Stanley Kok},
    journal={arXiv preprint arXiv:2405.01881},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01881},
    primaryClass={q-fin.RM cs.LG}
}