One of the major outcomes created by the financial crisis has undoubtedly been the marked rise in regulatory scrutiny, coupled with an increase in regulators taking action against firms falling short of the required standards. For the banking industry, this has resulted in global regulatory fines of around $321 billion being imposed over the last […]
By moving from tactical compliance to strategic investment, firms can not only satisfy the regulators, but also boost the analytical capabilities of the front office; delivering better execution, greater trading signal analysis and more efficient business intelligence.
MiFID II broadens the scope of the systematic internaliser (SI) regime, not only to cover more asset classes, but also to apply more stringent rules, with the aim of improving pre-trade transparency and improving price efficiency and client executions. To examine the far-reaching consequences and practical implications for liquidity seekers and providers, against a backdrop of tougher Best Execution […]
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.
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.
In this article we look at the future of middle-office systems for banks and trading firms. Many of these companies are now relying on hodgepodge solutions, systems that essentially rest upon legacy technology and have been in need of modernisation for years. Meanwhile, as regulatory requirements have increased, demands on middle office systems have risen. […]
The third video in our series on trade surveillance processes and technologies. In this we take a look at what the future holds, in particular how artificial intelligence, machine learning and behavioural modelling approaches can help predict and prevent manipulative or abuse market activity.