A BERT-Based Framework for Extracting Business Insights from Financial Reports

Financial reports bear a lot of textual information, which is normally unstructured and imperative to business decision-making. Nevertheless, it is difficult to draw meaningful conclusions out of such huge and sophisticated papers through the conventional methods of analysis. This paper is an attempt to provide a BERT-based system to extract business insights in financial reports in an automated manner. Using the ability of Bidirectional Encoder Representations of Transformer to contextualize textual information (annual reports, management discussions, financial statements, etc.), the model proposed then analyzes the textual information to extract important information (sentiment, risk factors, performance indications, etc.). The framework includes the data preprocessing, domain-specific weighting of a pre-trained BERT, and usage of classification and information extracting methods. The research experimental results prove that BERT-based system is much more operative than the old-style machine learning (ML) models in terms of accuracy, precision and F1-score. Moreover, the inferences that were to be made are of good assistance to all stakeholders- investors, managers and analysts for making sound decisions. The paper is relevant to the area of financial text analytics research because it provides a scalable and efficient framework in processing unstructured financial information. Future development can be based on real-time analytics and connection to financial decision support systems.