Machine learning tools are now in use at two-thirds of UK financial institutions, with the technology entering a new phase of maturity and more advanced stages of deployment, according to a survey conducted by the Bank of England.
The central bank and regulator FCA surveyed firms about the nature of deployment of ML - defined as 'the development of models for prediction and pattern recognition, with limited human intervention' - the business areas where it is used and the maturity of applications. It also collected information on the technical characteristics of specific ML use cases. Those included how the models were tested and validated, the safeguards built into the software, the types of data and methods used, as well as considerations around benefits, risks, complexity and governance.
The poll 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.
It found that two thirds of respondents report they already use it in some form. The median firm uses live ML applications in two business areas and this 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," states the BofE. "One third of ML applications are used for a considerable share of activities in a specific business area."
From front-office to back-office, ML is now used across a range of business areas, the survey found, with most common applications in anti-money laundering (AML) and fraud detection as well as in customer services and marketing. Some firms also use 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.
While a majority of users apply their existing model risk management framework to ML applications, any highlighted that these frameworks might have to evolve in line with increasing maturity and sophistication of ML techniques.
In order to foster further conversation around ML innovation, the BofE 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