How is machine learning affecting the financial services sector?

Dec 2, 2019 1:00:04 PM

The UK economy is increasingly powered by big data and technologies that enable platform business models, advanced analytics, smartphone tech and peer-to-peer networks. The financial services sector is heavily reliant on this data and has transformed its operating model to support the needs of the new tech age.

Central to coping with big data is machine learning which has wide-ranging applications that allow it to study immense quantities of data and solve complex problems at high speed. 

Machine learning supports new products and services, reduces market friction and inefficiencies, and ultimately helps customers by reducing costs and helping asset management firms become more responsive and effective.

In the first half of 2019, the Bank of England and the Financial Conduct Authority ran a joint survey of over 100 financial institutions including banks, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers, non-bank lenders and principal trading firms. Their goal was to understand the current use of machine learning in UK financial services.

Survey findings

While the survey is not representative of the entire economy or indeed the entirety of the financial services sector, it does provide a market snapshot with some interesting findings...

  • The average firm uses machine learning in two business areas and this is expected to double within the next three years.
  • Machine learning has mostly passed its initial development phase and is entering more ‘mature stages of deployment’.
  • Machine learning is being used across a range of business areas. It’s commonly used in anti-money laundering and fraud detection, and also customer-facing applications such as customer service and marketing.
  • Businesses see huge value in regulation being an enabler for further machine learning deployment, with many wanting additional guidance on interpreting current regulation.
  • Machine learning isn’t considered to create new risks for the industry, but rather to amplify existing ones. The biggest concern is model validation and governance frameworks not keeping pace with technological developments.
  • Firms use a variety of safeguards to manage the risks associated with machine learning. The most common safeguards are alert systems and so-called ‘human-in-the-loop’ mechanisms. These can be useful for flagging if the model does not work as intended (eg in the case of model drift, which can occur as machine learning applications are continuously updated and make decisions that are outside their original parameters).
  • Firms mostly design and develop machine learning applications in-house. However, they sometimes rely on third-party providers for the underlying platforms and infrastructure, such as cloud computing.


The report findings are consistent with the view that machine learning will be an important part of the way financial services are designed and delivered in the future. The technology will potentially be used in areas critical to financial markets and the safety, soundness and conduct of firms. But it will also be usefully applied in areas that are not critical from a regulatory point of view.

In addition, the report found that regulators will want to engage with, and build up, an understanding of the technical aspects of machine learning. The report cites that these could be issues around model risk management, which some respondents highlighted as a specific constraint to the deployment of machine learning. There are also questions regarding software and data validation, the governance, resilience and security of machine learning applications within financial services, as well as potential ethical issues that arise from the use of machine learning and novel data sources.

You can read the full survey report here.

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