Modern Approaches to Finance
Modern Approaches to Finance


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.


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.

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google