The promise of machine learning (ML) is to make financial services and markets more efficient, accessible and tailored to consumer needs.
The survey was sent to almost 300 firms including banks, credit brokers, e-money institutions, investment managers, insurers, non-bank lenders and principal trading firms.
Two thirds of respondents already use machine learning in some form. Respondents said they are already seeing benefits from ML and expect these to increase significantly over 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 and ML is 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 are also using ML in areas such as credit risk management, trade pricing and execution, as well as general insurance pricing and underwriting.
Regulation is not seen as a barrier but some firms stress 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.
Additionally, firms don't think that ML creates new risks but could be an amplifier of existing ones, such as applications not working as intended if frameworks do not keep pace with technological developments.
Firms said they are using a variety of safeguads to manage the risks associated with ML, the most common being alert systems and ‘human-in-the-loop’ mechanisms which can flag if the model does not work as intended.
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.
