Explainable Risk Classification in Financial Reports

TL;DR

This paper introduces FinBERT-XRC, an explainable deep-learning model for assessing financial risk from 10-K reports, providing multi-level explanations and outperforming existing methods in accuracy.

Contribution

The paper presents a novel explainable deep-learning model that offers multi-level interpretability for financial risk classification from 10-K reports, improving transparency and accuracy.

Findings

Outperforms state-of-the-art in predictive accuracy

Provides explanations at word, sentence, and corpus levels

Effective on a large real-world dataset spanning six years

Abstract

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.