The barriers to entry have fallen and FIs are no longer only testing and experimenting with machine learning (ML), a subset of AI that allows computers to perform tasks without explicit instructions and relies on patterns. ML is now being deployed in key departments such as risk management, pre-trade analytics and portfolio optimisation, for example.

Finextra spoke to Geoffrey Horrell, director of applied innovation at Refinitiv’s London Lab and Joe Rothermich, Refinitiv’s head of labs - Americas about their recent report ‘Smarter Humans. Smarter Machines: Insights from the Refinitiv 2019 Artificial Intelligence/Machine Learning Global Study’, how ML processes can be deployed in the cloud and how it has become an enabler of competitive advantage.

The AI explosion

Rothermich starts off with a comparison of AI to “the explosion of the Internet, when suddenly you had the ability to quickly scale up servers and create websites. I think we are starting to see that with data, AI and machine learning algorithms.

“In the past, there used to be a huge barrier to entry and although the machine learning algorithms haven’t changed dramatically since the early 2000s, we now have the ability to test out new ideas, train models and implement them in production systems easily.”

Traditional infrastructure prevents scalability and digital transformation, Rothermich explains, his team being one of the early adopters of Hadoop in financial services he explores how building the infrastructure and prepping the data required substantial up-front investment of time and equipment.

The industry has moved to the cloud in order to make data accessible immediately, so algorithms can be written and tested at a faster rate, which in turn lowers the cost of production. There is an extensive breadth of data across all asset classes used by Refinitiv Labs that has been extensively curated and enriched and as a result, is now ML ready.

Providing the productivity edge

Coupling access to this real time data in the cloud allows clients to receive new insights at a faster rate, for use in risk assessment, transaction analysis, regulatory reporting, for example. Rothermich discusses how data such as accounting data, market data and text mining of news, events, filings, and transcripts are used to predict the likelihood of a company defaulting on its debt within a year.

Rothermich adds that recent research using deep learning has allowed the model to generalise better and not be tied to fixed vocabularies and could even adapt to multiple languages. Refinitiv Labs is conducting research in other areas, such as M&A prediction, to combine fundamental and text data to predict financial events.

Financial use cases

The growth of easy to use cloud infrastructure, the open source Python ecosystem and capabilities that help with machine learning workflows allow FIs to test out new databases or new computing infrastructures easily.

“Implementing deep learning requires a lot more compute power and a lot more training data. We are working on this by using the cloud to scale up and conduct these experiments, leveraging machine learning frameworks without up-front investments in time and cost being an issue,” Rothermich says.

Returning to risk management, it is evident that the scalability of the cloud also allows FIs to process massive amounts of data and obtain a response at a rapid rate but from a regulatory standpoint, there are issues around data, requiring the traceability of experiments with data and proving there are no biases.

But what type of risk use case are we using machine learning to address? Horrell extends on the credit risk example to answer this question and states that “with investment risk, it’s about getting a much more real time view of probability of default compared to traditional credit ratings which tend to be lagging indicators.

“By the time you see substantial deterioration in a company’s fortunes that equates to a credit rating downgrade, the damage to the investment is already done. We know that there is more unstructured information out there that would give an early indicator to different kinds of financial distress or other leading indicators towards a higher probability of distress.

“You can incorporate additional types of information using machine learning, different models for different data sets must be maintained and many, many test iterations must be run through. You also have to have a large capacity to handle the data, and to backtest it to see whether that additional unstructured data can give you that early indication that there might be a problem with the company,” Horrell explains.

Sharing and parallelising with the cloud

In addition to this, while the cloud helps smaller teams become more agile when setting up a project and allows for faster experimentation, the cloud also allows FIs of all sizes to change direction when required, enhancing creativity and productivity.

“In the front office, new horizons have opened up in terms of the types of data financial services insitutions can now analyse to power their investment and trading strategies. The rise of alternative data feeds into that, and the cloud creates many opportunities to look at this data.

“The cloud can handle the scale of these datasets and provide the techniques and ML approaches to make sense of them and help FIs find completely new ways of generating investment ideas.”

Rothermich explains that “sharing code, sharing resources and sharing data is a lot easier in the cloud. And some of the tasks that are completed during a machine learning research project are very easily parallelisable and easy to scale up if the cloud resource is there.”

On parallelisation, Horrell continues to say that because of the flexibility of the cloud, the technology can be applied to areas where it would not normally be. For instance, multiple risk models can be run, and data can be analysed in different ways from a risk point of view.

Rothermich highlights that in conversation with hedge funds, they revealed that one of the biggest tasks that they face is evaluating new datasets in addition to ingesting, mapping and validating that data. “The cloud’s capacity for data has helped with loading up and merging new content sets and new, alternative datasets. This form of rapid data onboarding and evaluation gives FIs an informational edge.”

Democracy vs. data

While there is a definite democratisation emerging with anyone being able to access data in the cloud, Horrell adds that “ultimately, you cannot do data science without the data. The better quality your data, the better quality your results.”

Read ‘Smarter Humans. Smarter Machines: Insights from the Refinitiv 2019 Artificial Intelligence/Machine Learning Global Study’ here.