From ELIZA to ChatGPT: The Evolution of NLP and Financial Applications

Abstract

Natural language processing (NLP) has revolutionized the financial industry, providing advanced techniques for the processing, analyzing, and understanding of unstructured financial text. We provide a comprehensive overview of the historical development of NLP, starting from early rules-based approaches to recent advances in deep-learning-based NLP models. We also discuss applications of NLP in finance along with its challenges, including data scarcity and adversarial examples, and speculate about the future of NLP in the financial industry. To illustrate the capability of current NLP models, we employ a state-of-the-art chatbot as a co-author of this article.

KEY FINDINGS (1) The use of NLP in finance has evolved significantly over the past few decades with the growth of data, storage, and computational power. NLP is now used for wide range of sophisticated tasks including asset management, risk management, and impact investing.

(2) The development of deep-learning-based large-language models like GPT-3/ChatGPT have significantly advanced the applications of NLP in finance. These models have the ability to understand and generate human-like language, making them useful for various tasks, including assisting in the writing of this article.

(3) Solving problems related to data bias, high computational needs, and inaccurate responses generated by the models will make NLP models even more accessible and indispensable.