Exploring Machine Learning and Advanced Modeling Techniques in Financial Data Science

This paper aims at discussing how financial data science revolutionized the financial industry with specific emphasis on supervised, unsupervised, and reinforcement learning. It focuses on new modeling techniques, such as time series analysis and deep learning used in algorithmic, trading, risk assessment, and fraud identification. This paper also discusses several important issues like data quality problem, model interpretation issue, and computational cost issue. Apart from that, it explores about the recent advancements including blockchain and quantum computing with emphasis on legal aspects of privacy concerns. Finally, the survey emphasizes the need for developing new approaches to financial data science accompanied by the challenges arising with its application.