Discussion into possible use cases for deep learning within financial services, particularly in risk and investment management, as well as the challenges that arise from an infrastructure and data storage perspective.
Artificial Intelligence in Practice - Deploying AI Systems into a Live Environment. What are some of the key considerations when deploying Artificial Intelligence and Machine Learning-based systems into a production environment?
The complexities surrounding the Banking, Finance and Insurance sector today have led to a significant growth in the use of grid computing and high-performance computing (HPC) for computationally-intensive tasks. What are the key considerations that firms should take into account when putting together the necessary infrastructure to support their computationally-intensive needs?
The complexities surrounding the Banking, Finance and Insurance sector today have led to a significant growth in the use of grid computing and high-performance computing (HPC) for computationally-intensive tasks. These are many and varied, and include areas such as derivative pricing, risk analytics, quantitative modelling, portfolio optimisation, and bank stress testing.
The use of Artificial Intelligence is becoming increasingly widespread in the financial services industry. Whether it’s in the research and development of trading strategies, analysis and management of risk, or assisting with regulatory and compliance functions, there are a growing number of use cases for AI and machine learning. What are the implications of this […]
In this article, Mike O’Hara, publisher of The Trading Mesh, examines the infrastructure needs for financial markets firms as use cases for artificial intelligence and machine learning expand, with John Denheen of Tyler Capital, KPMG’s Robert Mirsky, Trade Informatics’ Aaron Schweiger, Sybenetix’s Taras Chaban, Jan Machacek of Cake Solutions, Michael Cooper of BT Global Banking […]