Machine learning on the rise in financial services

The use of machine-learning is gathering across the financial services industry.
12 April 2019

One of the world’s largest banking organizations. Source: Shutterstock

The financial services industry has always been a fast mover when it comes to the adoption of new technologies.

In a new report, financial data provider Refinitiv said more than 90 percent of the organizations it surveyed had either deployed machine learning (ML) in multiple areas of the business or have made a start in certain pockets.

ML refers to the use of algorithms and statistical models in financial markets without using human directions and instead relies on patterns to make choices.

While the initial driver of such technologies was the automation of repetitive tasks, the survey found that the top applications were in the areas of risk avoidance, generating trading and investment ideas, and analyzing performance.

Conducted via 447 telephone interviews of senior executives and data-science practitioners across various financial services firms, the survey also found the quality of data as the primary barrier to machine-learning adoption.

“Thanks to parallel computing and cloud computing, we are seeing the playing field being slowly leveled in terms of machine-learning strategies,” said Tim Baker, global head of applied innovation at Refinitiv.

Regarding ML adoption rates, 90 percent have deployed in at least some areas of the business, while the remaining 10 percent are experimenting and investing in infrastructure and people.

Seventy-eight percent of businesses think that the use of ML is a core component of business strategy and 75 percent of them think they will make a significant investment in ML. According to the report, the main driver behind ML adoption rates is to make informed decisions with better quality data (60 percent).

The desire to increase productivity and speed is second (48 percent), while cost-cutting checks in at 46 percent as the third most important reason.

Machine learning enables you to rapidly model and test multiple trading strategies using different risk scenarios. But, the report also notes, it is important to ensure that the industry doesn’t move from human-based errors to systematic machine-based ones.

In the rush to adopt new technologies and techniques, Refinitiv recommends businesses focus on the quality of the data that is feeding the models, and on understanding where biases can occur.

In many areas of the survey, the financial services professionals from North America are more advanced than those in Europe and Asia. The innovation in AI and machine learning has also come largely from North America, out of universities such as Stanford, Berkeley, and MIT.

Another reason for this current lead is that the financial market in North America is more homogeneous than in the rest of the world.

However, the challenging quest for quality sources of data to use in machine learning is likely to continue into the foreseeable future. While it is important to be on the lookout for new data assets, this can sometimes be like searching for a needle in the haystack.

It is equally, if not more, important to ensure the data you are using is of the highest quality possible. For in the end, data is just the beginning.