The AI and machine learning trends transforming financial services have been revealed in our inaugural survey of global business leaders and data scientists. Find out how and where the technology is being deployed, as well as the biggest barriers to adoption.
- The inaugural Refinitiv survey of 450 financial professionals reveals the latest AI and machine learning trends, confirming that the technology is now an integral part of business.
- Machine learning is deployed in financial risk management, pre-trade analytics and portfolio optimisation, but poor quality data is still a barrier to wider adoption.
- The survey also breaks down regional AI and machine learning trends, with financial institutions in North America leading adopters of #MLreadydata.
We’re in the midst of the fourth industrial revolution: the fusion of the physical, digital and biological enabled by data. Profound shifts, facilitated by technologies such as artificial intelligence and machine learning, robotics and the Internet of Things, are already underway.
The future will be drastically different from what we know today.
Emerging technologies will underpin the formation of new human-machine partnerships, which will help humans transcend their limitations, enhance daily activities and reset expectations for learning and work. This is the actualisation of smarter humans, smarter machines.
The results of Refinitiv’s inaugural Artificial Intelligence/Machine Learning survey reveal that machine learning will be the single greatest enabler of competitive advantage in the financial services sector.
We’ve seen an explosion in machine learning trends over the past few years, led by applications for image processing, natural-language processing (NLP) and machine translation.
As these new capabilities are largely based on open-source libraries, and can be deployed relatively cheaply in the cloud, the barriers to entry have fallen dramatically.
We expect a flurry of commercial and product innovation from organisations of all sizes. The benefits will extend well beyond automating rules-based repeatable tasks once done by humans.
Our survey of financial institution leaders and data scientists confirms that machine learning is now an integral part of running a business.
Financial institutions have gone beyond experimenting with and testing machine learning. They are now deploying it in key areas such as financial risk management, pre-trade analytics and portfolio optimisation.
David Craig, Refinitiv CEO, says: “This survey confirms the important role AI and machine learning play in the transformation of financial services and can aid your organisation on its technology journey. In the end, data is just the beginning.”
Poor quality data
You may be surprised to also learn just how much the quality of data matters. Poor quality data is cited as the biggest barrier to the adoption and deployment of machine learning.
Unstructured data, as well as data from alternative sources, are increasingly important areas but need considerably more work before their insights are truly reliable. The adage ‘garbage in, garbage out’ has never been more pertinent.
If data is the new oil, then much of it still needs a lot of refining and that’s a heavy lift for the consumers of data.
Boardroom and the data lab
Data scientists are tasked with creating the models and algorithms that will set their organisations apart from the competition.
But there is a mismatch between the vision in the boardroom and the reality on the ground. C-level professionals believe it is important to be seen using the latest tools and techniques for competitive advantage and may be overstating the company’s actual adoption of AI and machine learning.
Data scientists, on the other hand, are under pressure to deliver on the promise of machine learning but must navigate real organisational constraints. The two agree that machine learning is essential but vary somewhat in terms of their organisation’s current state.
Global machine learning trends
There’s also a disparity between how technologies are being adopted and used around the world. Financial institutions in North America are the front runners.
Asian institutions are more advanced in some areas than in Europe, such as in machine learning being core to business strategy and the projected growth in numbers of data scientists.
However European organisations lead those in Asia in terms of having deployed machine learning.
This could be due to the fact that the underlying capabilities originated in North America – from the development of the first algorithm for random forests (developed by Tin Kam Ho at Bell Labs in 1995), to the open sourcing of tools such as TensorFlow.
Time will tell whether or not open-source technologies will extend beyond geographic boundaries to level the playing field.
Buy-side leads with #MLreadydata
Similarly, the study suggests that the buy-side is ahead of the sell-side, but the latter is expanding its use of machine learning technology. This reflects the reality of hedge funds traditionally investing more in machine learning in their search for alpha.
Again, the greater availability of advanced tools will likely level the playing field – on both buy- and sell-sides – over time.
Learn more about the Artificial Intelligence/Machine Learning Survey and what smarter humans and smarter machines will mean for your firm and the future of the industry.