Explainable Risk Classification in Financial Reports
This addresses the need for transparent and accountable risk classification in financial decision-making, offering an incremental improvement with enhanced interpretability.
The authors tackled the problem of automatically assessing post-event return volatility risk from 10-K financial reports, proposing FinBERT-XRC, an explainable deep-learning model that provides multi-level explanations and achieves state-of-the-art predictive accuracy on a large real-world dataset.
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.