Two-thirds of financial services businesses in the UK are using machine learning (ML) to replace manual tasks, according to research by the Bank of England and the Financial Conduct Authority (FCA).
On average, firms use live ML applications in two business areas and this is expected to more than double within the next three years.
The findings are based on a survey of 106 banks, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers, non-bank lenders and principal trading firms. Deployment was found to be most advanced in the banking and insurance sectors.
The Bank of England defines ML as the development of models for prediction and pattern recognition, with limited human intervention.
ML is most commonly used in anti-money laundering and fraud detection, and in customer-facing applications such as 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.
In terms of managing the potential risks associated with ML, 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 (e.g. in the case of model drift, which can occur as ML applications are continuously updated and make decisions that are outside their original parameters).
“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,” the report said.
“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.”
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