A financial services survey from the UK’s regulators has revealed that legacy systems and data limitations are the biggest constraint in terms of machine learning (ML) innovation – especially so in banking and insurance.

The Bank of England (BoE) and Financial Conduct Authority (FCA) conducted a joint survey this year to better understand the current use of ML in UK financial services. It was sent to almost 300 firms, including banks, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers, non-bank lenders and principal trading firms, with a total of 106 responses received.

Three quarters stated that existing rules were not seen as a barrier, but some firms stressed the need for additional guidance on how to interpret current regulation. “The biggest reported constraints are internal to firms, such as legacy IT systems and data limitations,” the report noted.

Of the respondents that did consider regulations to be a constraint, the most common issues cited are around model risk management and the need to adapt processes and systems to cover ML-based models. Some firms noted challenges of meeting regulatory requirements to explain decision making when using so-called ‘black box’ ML mode.

Firms thought that ML does not necessarily create new risks, but could be an amplifier of existing ones. Such risks, for instance applications not working as intended, may occur if model validation and governance frameworks do not keep pace with technological developments.

The research showed that firms use a variety of safeguards to manage the risks associated with ML, the most common being alert systems and so-called ‘human-in-the-loop’ mechanisms. These can be useful for flagging if the model does not work as intended, like in the case of model drift, which can occur as ML applications are continuously updated and make decisions that are outside their original parameters.

The majority (76 per cent) of ML use cases are developed and implemented internally by firms, with the remaining 24 per cent implemented by third-party provider. However, firms sometimes rely on third-party providers for the underlying platforms and infrastructure, such as cloud computing.

The majority of users apply their existing model risk management framework to ML applications, but many highlighted that these frameworks might have to evolve in line with increasing maturity and sophistication of artificial intelligence techniques.

This was also highlighted in the BoE’s response to the Future of Finance report. In order to foster further conversation around ML innovation, the BoE and the FCA have announced plans to establish a public-private group to explore some of the questions and technical areas covered in the report.

Two thirds of respondents report they already use ML in some form, with the median firm using live ML applications in two business areas – something which is expected to more than double within the next three years.

In many cases, ML development has passed the initial development phase, and is entering more advanced stages of deployment. One third of ML applications are used for a considerable share of activities in a specific business area, with deployment most advanced in the banking and insurance sectors.

ML is most commonly used in anti-money laundering and fraud detection, as well as in customer-facing applications. Some firms also use ML in areas such as credit risk management, trade pricing and execution, as well as general insurance pricing and underwriting, the report added.

“The promise of ML is to make financial services and markets more efficient, accessible and tailored to consumer needs,” wrote the BoE. “At the same time, existing risks may be amplified if governance and controls do not keep pace with technological developments.

“More broadly, ML also raises profound questions around the use of data, complexity of techniques and the automation of processes, systems and decision-making,” the report added.

As announced by governor Mark Carney in his recent Mansion House speech, based on the survey findings and with an increased level of understanding, the BoE will explore potential policy areas relating to ML. “We will also consider repeating this survey in 2020,” the report concluded.

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