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Regulatory and legacy system constraints hampering machine learning deployment

Regulatory and legacy system constraints hampering machine learning deployment

UK financial firms blame a lack of regulatory clarity and legacy systems for constraining deeper deployment of machine learning technology, according to a survey conducted by the Bank of England.

Almost half of the 71 firms who responded to the survey said there are Prudential Regulation Authority and/or Financial Conduct Authority regulations that constrain ML deployment. A quarter of firms (25%) said this is due to a "lack of clarity" within existing regulation.

Regulatory restrictions notwithstanding, the greatest barrier to ML adoption and deployment cited by respondents was legacy systems

Despite the contraints, almost three quarters of firms reported using or developing ML applications, which are becoming increasingly widespread across more business areas.

This trend looks set to continue and firms expect the overall median number of ML applications to increase by 3.5 times over the next three years. The largest expected increase in absolute terms is in the insurance sector, followed by banking.

The most commonly identified benefits cited by respondents are enhanced data and analytics capabilities, increased operational efficiency, and improved detection of fraud and money laundering.

Respondents do not see ML, as currently used, as high risk. The top risks identified for consumers relate to data bias and representativeness, while the top risks for firms are considered to be the lack of explainability and interpretability of ML applications.

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