success – call for proposals - gov. uk. 6. pra staff background paper – the links between prudential regulation, competitiveness and growth, september 2023. 7. pra staff background paper – how to measure the contribution of prudential regulation to competitiveness and growth, september 2023. 8. for example we have published several consultation papers ( cps ) since february : see pra consultation paper 4 / 23 the strong and simple framework : liquidity and disclosure requirements for simpler - regime firms, february 2023, proposing simplifications to liquidity, reporting, and disclosure requirements for firms eligible for the simpler regime ; see pra consultation paper 5 / 23 - remuneration : enhancing proportionality for small firms, february 2023, which proposes simplifications to remuneration rules for smaller firms ; see pra consultation paper 12 / 23 - review of solvency ii : adapting to the uk insurance market, a cp on the review of solvency ii. moreover, we have consulted on the so - called ‘ bonus cap ’, see pra consultation paper 15 / 22 - remuneration : ratio between fixed and variable components of total remuneration ( ‘ bonus cap ’ ), december 2022. we are working on publishing the final policy. 9. there is evidence that higher capital requirements can have a short - term negative impact on output, while exerting a positive impact in the medium and long term. see for example jorda, o et al. ( 2021 ), bank capital redux : solvency, liquidity, and crisis, the review of economic studies, 88 ( 1 ), pages 260 – 286. 10. international monetary fund, financial sector assessment program ( fsap ). financial sector assessment program ( fsap ) ( imf. org ). 11. basel committee on banking supervision, regulatory consistency assessment programme ( rcap ). regulatory consistency assessment programme ( rcap ) handbook for jurisdictional assessments ( bis. org ). 12. authorisations | bank of england. 13. all firms, deposit - taking firms, insurance firms. 14. see dear ceo letter : transforming data collection - an update on progress and plans for 2021, february 2021. 15. see bank of england staff working paper no. 834 - the language of rules : textual complexity in banking reforms, november 2021. 16. see prudential and resolution policy index | bank of england. 17. see the

immediate problems and challenges precipitated by the crisis. for example, many businesses, including financial firms, are looking to use ai, sometimes combined with alternative data sources, for enhancing customer engagement, for driving more automation of internal processes, and for improving virtual working environments. in a recent survey of the banks and insurers we regulate, conducted as part of our ongoing monitoring of the impact of covid - 19 by the bank ’ s fintech hub along with colleagues from the pra, around 45 % of those firms that participated reported that the crisis has led to an increase in the importance of ai and data science applications for their future operations, with around 55 % reporting no change, and none noting a decrease. the survey also looked at changes in firms ’ investment plans and resource commitments for ai projects as well as areas where the crisis has had an impact on existing applications across different business lines. all speeches are available online at www. bankofengland. co. uk / news / speeches and @ boe _ pressoffice while the use of ai has clear benefits in an increasingly data - driven economy, there are risks and challenges. and the impact of those benefits and risks will be felt at different paces and different depths at technological, social, corporate, and systemic levels. it ’ s useful to think of the risks and challenges in a hierarchical way, starting with those that may become apparent at the data level and building up to the level of models trained on those data ; then, widening to the level of the firm, and finally, up to the level of the financial system as a whole. overlaying this are the regulatory and legal challenges presented by ai. taking these in turn, data form the core of all ai models and good data management and data governance are essential in controlling issues with, for example, biases that may be embedded in the data. at the model level, we need to think about issues such as ensuring that models continue to perform under a wide range of different conditions and being able to explain the outputs of complex ai systems. moving to the firm level, some of the key issues will revolve around risk management and governance structures ; around accountability and appropriate controls. and finally, for the financial system as a whole, ai may amplify network effects such as unexpected changes in the scale and direction of market moves. in terms of the regulatory challenges, it ’ s clear that policy thinking in this arena is also evolving rapidly. but the existing