This video is part of the Deep Learning in Finance Summit, London 2016 Event. If you would like to access all of the videos please click here.

Research to Products: Machine & Human Intelligence in Finance

Artificial intelligence and deep learning in finance has gained traction in the past years. This talk will cover our work in the field of machine learning applied to distress events, networks and news. We look into machine learning for systemic risk identification and distress signalling by measuring excessive increases in micro and macro-financial imbalances, network analytics to account for the interconnectedness of financial markets and deep learning textual data for event extraction with a focus on bank distress in the news.

Peter Sarlin, Associate Professor at Hanken School of Economics

Peter is an Associate Professor of Economics at Hanken School of Economics (Helsinki, Finland), and Director of RiskLab Finland. Currently, he is a research associate with the Systemic Risk Center at London School of Economics (LSE) and IWH Halle Institute for Economic, as well as a board member of the IEEE Analytics and Risk Technical Committee and the IEEE Computational Finance and Economics Technical Committee. He is also an Associate Editor of Journal of Network Theory in Finance and Intelligent Systems in Accounting, Finance & Management. Peter received his PhD (Econ) from the Department of IT, Åbo Akademi University, and has also studied at LSE, Stockholm School of Economics and Stockholm University. He has been a Financial Stability Expert and advised several central banks, such as the European Central Bank. Peter’s book Mapping Financial Stability was published by Springer in May 2014.