Recent Developments in Deep Learning in Finance
This talk aims to provide a literature survey of published use cases and research papers on use of machine learning in finance and how it is helping re-focus the financial sector to its fundamental purpose. The discussion will in particular focus on recent developments in deep learning applications and put a spotlight on some of the relevant research in deep learning and reinforcement learning. Other aspects like generating synthetic data, text analytics, transfer learning and explainability of deep learning models will also be discussed. This talk will conclude with update on evolving regulatory landscape and some ethical questions about use of these models.
Harsh currently works with Morgan Stanley in Quant Analytics Group. He started his career as a programmer focussed on developing data driven algos in the areas of speech recognition, image processing and bioinformatics. He then moved to financial risk management and over the last 12 years has worked in various roles through the life cycle of models. In these roles, he has been continuously enthusiastic to applying machine learning in problems related to behavioural assumptions, data quality, recommender systems, model benchmarking and text analytics. His current role requires him reviewing all Machine Learning models used by the firm and providing direction to shaping AIML governance framework and strategy. He is also a visiting lecturer with universities and training institutions.