Revolutionizing Finance with LLMs: An Overview of Applications and Insights

TL;DR

This paper provides a comprehensive overview and evaluation of how Large Language Models like GPT-4 are transforming finance through automation, analysis, and personalized services, highlighting current applications and future prospects.

Contribution

It offers a detailed survey of LLM applications in finance and presents holistic tests demonstrating GPT-4's effectiveness across various financial tasks.

Findings

GPT-4 effectively follows prompts in financial tasks

LLMs enhance operational efficiency and customer satisfaction in finance

The study identifies new research opportunities in financial applications of LLMs

Abstract

In recent years, Large Language Models (LLMs) like ChatGPT have seen considerable advancements and have been applied in diverse fields. Built on the Transformer architecture, these models are trained on extensive datasets, enabling them to understand and generate human language effectively. In the financial domain, the deployment of LLMs is gaining momentum. These models are being utilized for automating financial report generation, forecasting market trends, analyzing investor sentiment, and offering personalized financial advice. Leveraging their natural language processing capabilities, LLMs can distill key insights from vast financial data, aiding institutions in making informed investment choices and enhancing both operational efficiency and customer satisfaction. In this study, we provide a comprehensive overview of the emerging integration of LLMs into various financial tasks. Additionally, we conducted holistic tests on multiple financial tasks through the combination of natural language instructions. Our findings show that GPT-4 effectively follow prompt instructions across various financial tasks. This survey and evaluation of LLMs in the financial domain aim to deepen the understanding of LLMs' current role in finance for both financial practitioners and LLM researchers, identify new research and application prospects, and highlight how these technologies can be leveraged to solve practical challenges in the finance industry.