Practical Applications and Development Challenges of Artificial Intelligence in Finance

The financial industry has a strong demand for efficient and accurate data analysis and decision-making, and the application of artificial intelligence in it is becoming increasingly widespread. This article reviews the practical application and development bottlenecks of artificial intelligence in the financial field. In terms of practical application, artificial intelligence has achieved remarkable results in many fields such as risk assessment and management, investment decision support and customer service. For example, it enhances credit evaluation, improves investment strategies through smart advisors, and boosts customer interaction via chatbots and profiling tools. However, challenges remain, including data complexity and privacy issues, algorithmic bias, lack of explainability, and talent shortages. Regulatory gaps also create compliance risks. The research conclusions show that the application of artificial intelligence in the financial field has significant advantages, but also faces many challenges. In order to achieve its sustainable development, multiple parties need to work together to optimize algorithms, strengthen talent training, improve the regulatory system, break through development bottlenecks, and promote the steady development of the financial industry. The research aims to provide theoretical reference and practical guidance for promoting the sustainable development of artificial intelligence in the financial field, which is of great significance to financial institutions in enhancing their competitiveness and preventing financial risks.