The Application of Machine Learning in Finance: Situation and Challenges

Abstract

Since its development in the 1950s, machine learning (ML) has rapidly evolved from a theoretical concept into a practical tool, finding wide application in key areas of the financial industry, including market forecasting, risk management, and investment strategy optimization. In recent years, deep learning (DL), a significant branch of ML, has gained a prominent position in the financial sector due to its superior performance in handling complex data and executing financial tasks. This paper reviews the major applications of ML and DL in the financial domain, analyzing their technical advantages, challenges, and future development trends. Key areas of application include market trend prediction, credit risk assessment, quantitative investment, and fraud detection. At the same time, issues such as the complexity of ML models, data privacy, and model interpretability continue to pose challenges for its widespread adoption in the financial industry. In the future, with further technological innovations and cross-domain integration (e.g., quantum computing and blockchain), ML is expected to bring about significant transformations in the financial sector.