Abstract: Given the high volume of accurate historical data and the underlying quantitative nature of the industry, the financial sector is well suited for machine learning. Today data science has found many integral roles in the financial ecosystem, from automating large scale text classification and entity extraction pipelines, the use of sentiment analysis to improve revenue forecasting, to state-of-the-art deep learning algorithms for stock market predictions and portfolio optimizations. In this overview, I will discuss these applications and show how these algorithms are being used across all major sub-disciplines of machine learning: including clustering, time-series analysis, topic modeling, natural language processing and machine vision.
Bio: Michael Segala is the CEO and co-founder of SFL Scientific, a data science consulting firm that specializes in big data solutions. His firm leverages advanced machine learning and analytics techniques to provide insight into numerous industry-spanning problems, from healthcare to stock market prediction. Before founding SFL Scientific, Michael worked as a data scientist in several well-known tech companies, such as Compete Inc. and Akamai Technologies. He holds a PhD in Particle Physics from Brown University.