The Role of Natural Language Processing in Extracting Key Insights from Financial Reports

Abstract

Natural Language Processing (NLP) has recently brought significant advancements in the analysis of financial reports as part of artificial intelligence technologies. Given the vast amount of textual data in financial reports, using NLP techniques helps analysts obtain key information more accurately and quickly. The objective of this article is to explore the role of NLP in enhancing financial report analysis and improving the speed and accuracy of financial decision-making. This article uses a descriptive analysis method to examine various applications of NLP in the financial domain. The data used includes scientific articles and published reports on NLP applications in financial report analysis. Techniques such as text analysis, summarization, and classification of texts are reviewed to extract key insights from financial reports. The findings reveal that NLP significantly improves the accuracy of financial analyses by using techniques like sentiment analysis, text classification, and summarization to extract key information more efficiently. Furthermore, NLP helps reduce human errors and enhances financial decision-making by providing faster access to crucial data. NLP is poised to become a key tool in future financial analyses. With the continued development of related technologies such as deep learning and artificial intelligence, the accuracy and speed of financial report analysis are expected to increase, creating new opportunities in the financial sector.