As the financial industry experiences rapid growth in data volume and increasing complexity, traditional analytical methods find it challenging to fulfill the requirements for efficient and accurate decision-making. Intelligent data analysis technologies like machine learning and big data analytics have been widely applied in the financial sector, enhancing data processing capabilities and decision-making efficiency in areas including financial forecasting, credit evaluation, and investment decisions. In addition, these technologies are instrumental in enhancing risk management, optimizing resource allocation, and fostering innovation within financial services. This paper reviews the research progress in this field, analyzes the theoretical foundations, technical approaches, and key achievements of various methods, and summarizes the current critical challenges, including data quality and privacy protection, model interpretability, algorithmic bias, and regulatory adaptation. The results indicate that despite the improvements in accuracy and automation brought by intelligent data analysis in financial operations, issues concerning transparency, security, and effective deployment remain major challenges. Future efforts can advance the effective application of these technologies in finance by optimizing data governance, improving model robustness, fostering interdisciplinary collaboration, and refining regulatory frameworks.
Yu Zhu (2025) studied this question.