Already, there are dozens of examples where both buy- and sell-side institutions are investing in trials and full-production deployments to help identify trading strategies, optimize collateral management, and improve the measurement of counterparty risk.
To be sure, complex hurdles still must be overcome before Wall Street firms can deploy machine-learning techniques at a scale that will have significant impact on their businesses. To start, institutions will need to have a well-structured big data program and associated infrastructure. Even if they have invested in their data capabilities, global data privacy regulations may make it difficult for them to leverage the information effectively unless they think strategically. Financial-services institutions must also improve their ability to recruit scarce tech talent as they are competing with the likes of Google, Amazon, and emerging fintechs for talent.
But financial-services firms would do well to begin to familiarize themselves with where machine learning is taking hold, and consider investing in areas where the new technology could eventually provide them with a competitive advantage.
In the report below, we examine the potential applications for machine learning and list where we are seeing it start to make a meaningful difference.
The world in which intelligent robots replace humans at scale is still imaginary – for now – but the line between how traditional capital markets firms operate and the techniques in common use by technology companies will only continue to blur
