Machine learning is already utilized by financial companies to tackle complex decision problems, analyze large datasets, and predict future price trends. As a powerful tool to overcome statistical problems, machine learning has brought many benefits to finance, including risk management, fraud detection, and algorithm trading. However, deep learning also poses significant challenges, such as interpretability and instability, that must be addressed. This section (1) explores applications of machine learning techniques in the financial industry, with an emphasis on big data and simulations, (2) covers various aspects of machine learning, including its benefits, challenges, and practical applications, and (3) provides an overview of the current state of deep learning in finance and the challenges that must be addressed.

The section invites original research papers on machine learning and its applications to finance, focusing on innovative ways of using machine learning and data mining techniques to address financial problems, such as investment decision-making, forecasting, macroeconomic analysis, asset credit evaluation, and analysis of large volumes of data. Potential topics for research include quantitative finance and artificial intelligence, pricing derivatives and fixed-income securities, algorithmic trading, forecasting, underwriting, credit intermediation, deep pricing, deep hedging, the combination of finance theory and machine learning, and comparison of machine learning techniques with traditional approaches in finance.