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.
When it comes to technology solutions for trading infrastructure, the build versus buy debate is one that never seems to go away. In the past, it was fairly common practice for banks and electronic trading firms to want to build and to run everything themselves. But more recently, there seems to be a definite trend […]
Many middle-office systems at banks and brokers still rely on a mishmash of spreadsheets and legacy systems, some of which have been in need of modernisation for years. How can firms embrace technology to improve efficiency and reduce costs in this area?
The trend of financial firms – on both the sell side and the buy side – outsourcing their trading infrastructure to specialist providers at co-located data centres. The prospect of outsourcing such a vital part of a trading firm’s operations has enormous implications, from cost to performance to business models.
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?