Financial Statement Text Information Mining and Key Information Extraction Model Construction

Abstract

Financial statement text information mining and key information extraction model design are critical areas of research that aim to use advanced computational approaches to extract important insights from textual data contained in financial documents. In this work, they look at methodologies, techniques, and applications that combine natural language processing (NLP) and machine learning to automate financial statement interpretation. To lay the groundwork for the research, researchers first conduct a thorough examination of existing literature in interdisciplinary domains such as computational linguistics, information retrieval, and finance. Building on insights from earlier studies, they design and use unique NLP approaches, such as named entity identification, syntactic parsing, sentiment analysis, and topic modelling, to extract essential financial metrics from textual data. Additionally, they create machine learning models that are suited to the peculiarities of financial terminology and reporting standards, combining domain-specific knowledge with linguistic experience to improve accuracy and reliability. They demonstrate the efficacy and scalability of the technique in automating the extraction of crucial financial information, such as revenue trends, cost patterns, and risk factors, through rigorous testing on real-world financial data. These results highlight the transformative power of natural language processing and machine learning in financial analysis, providing stakeholders in finance and accounting with actionable intelligence for informed decision-making, risk assessment, and compliance monitoring. By bridging the gap between computational linguistics and financial analysis, this study advances financial text analysis and provides the framework for future research and innovation in this emerging field.