Large Language Models (LLMs) are increasingly being applied in the financial sector, offering solutions for financial report generation, market trend forecasting, investor sentiment analysis, and personalized financial advice. These models, built on the Transformer architecture, leverage natural language processing to extract insights from vast financial data, aiding institutions in making informed investment decisions and improving operational efficiency. This study provides a comprehensive overview of LLMs' integration into various financial tasks, including financial engineering, forecasting, risk management, and real-time question answering. It also evaluates the effectiveness of GPT-4 in these tasks, highlighting its ability to follow prompt instructions across diverse financial applications. The paper discusses the challenges of applying LLMs in finance, such as the complexity of financial data and the need for high accuracy in predictions. It also explores the potential of LLMs in tasks like named entity recognition, sentiment analysis, question answering, time series forecasting, and mathematical reasoning. The study emphasizes the importance of combining LLMs with expert systems and manual review to ensure accuracy and reliability. Overall, LLMs are becoming a powerful tool for addressing financial challenges, offering new opportunities for innovation and efficiency in the financial industry.Large Language Models (LLMs) are increasingly being applied in the financial sector, offering solutions for financial report generation, market trend forecasting, investor sentiment analysis, and personalized financial advice. These models, built on the Transformer architecture, leverage natural language processing to extract insights from vast financial data, aiding institutions in making informed investment decisions and improving operational efficiency. This study provides a comprehensive overview of LLMs' integration into various financial tasks, including financial engineering, forecasting, risk management, and real-time question answering. It also evaluates the effectiveness of GPT-4 in these tasks, highlighting its ability to follow prompt instructions across diverse financial applications. The paper discusses the challenges of applying LLMs in finance, such as the complexity of financial data and the need for high accuracy in predictions. It also explores the potential of LLMs in tasks like named entity recognition, sentiment analysis, question answering, time series forecasting, and mathematical reasoning. The study emphasizes the importance of combining LLMs with expert systems and manual review to ensure accuracy and reliability. Overall, LLMs are becoming a powerful tool for addressing financial challenges, offering new opportunities for innovation and efficiency in the financial industry.