Two-thirds of UK financial services firms are using machine learning

The use of machine learning in the finance sector is set to increase as obstacles to its take up are not seen as being insurmountable

Finance firms are using machine learning (ML) across their businesses with the technology’s spread likely to continue as the obstacles it faces are not deemed insurmountable, according to a survey carried out by the Bank of England.

The survey, which had more than 100 respondents, revealed that around two-thirds of financial services companies in the UK are using ML. The technology is used to replace manual tasks in the industry through its ability to recognise patterns in data and make decisions.

“ML has wide-ranging applications in financial services and, when combined with increasing computational power, has the ability to analyse large data sets, detect patterns and solve problems at speed,” said the Bank of England.

“The use of ML has the potential to generate analytical insights, support new products and services, and reduce market frictions and inefficiencies. If this potential is achieved, consumers could benefit from more tailored, lower cost products and firms could become more responsive, leaner and effective.”

According to the report on the survey, ML is being used throughout businesses. In the back office ML is being used in risk management and compliance, while in the front office it is applied to customer management and even automating trading.

The expansion looks set to continue as 19% of companies have or are creating dedicated centres of excellence to promote ML across the organisation. Furthermore, companies said they are not being hindered by regulation. The biggest constraints are actually internal, according to the companies that responded to the survey, including the legacy IT systems used and limits on data.

“However, it is important to note that, overall, respondents do not perceive there to be major constraints to ML deployment,” said the Bank of England. “Firms do not consider the constraints – for example, those associated with older IT systems – to be insurmountable.”

Companies in banking and insurance are the biggest users of ML technology, with all 17 insurance companies that responded using it, and 22 out of 30 banks doing so.

The survey found that most companies largely design and develop ML applications in house. If they decide to use third parties, there is a lot of choice in banking and insurance.

Vast amounts of money is being pumped into financial technology (fintech) and insurance technology (insurtech) suppliers, which support the banking and insurance industries with digital innovation. Machine learning is a core technology in these suppliers that use data to create products and services tailored to individuals, and which are highly automated.

According to management consultancy Opimas, financial services firms have deep pockets when it comes to technology such as ML. It said finance firms in the investment sector spent $1.5bn on artificial intelligence (AI) technologies, including ML, in 2017. It predicted this would increase by 75% to $2.8bn in 2021.

Opimas said technologies such as machine learning, deep learning and cognitive analytics will replace 230,000 jobs in investment banking by 2025. The asset management sector will be hardest hit, with 90,000 people being replaced, it said.

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It's great to finally see financial companies making headway in implementing ML systems. The next decade will also see adoption of easy-to-use low or no-code MLaaS tools to bring into the fold non-Data Scientists.
Atakan Cetinsoy
BigML - Machine Learning made easy and beautiful for everyone

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AI washing is a poor substitute for insight. The mechanics of financial algorithms are not that complex, the real complexity lies in how real people handle the problems. In a stable market it is relatively easy to introduce statistics but throw in a left field event and it all collapses. Politics, fashions, egos and human fallibility steer markets not some easy number crunching.
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