##a rulebook contains 638, 000 words – 77, 000 words longer than war and peace in english translation. the complexity of the language used can make the text difficult to read. another layer of complexity is added because of cross - references and links between different parts of the rulebook, requiring the reader to refer backwards and forwards, disrupting reader flow. figure 2 is a visualisation of the rulebook. each node is a part the pra rulebook. each line between the nodes is a cross - reference in the text. when parts of the rulebook are linked together, tweaking one part can have unintended consequences for others. we can quantify the interconnectedness of different parts of the rulebook using the pagerank algorithm, the same algorithm used by google ’ s search engine. a higher score implies greater connectivity of a particular part to other parts. happily, most parts of the rulebook are self - contained and ‘ ‘ structurally simple ’. ‘ looking further into the future, a bigger win might be to automate the rulebook entirely. regulated banks are required to submit large quantities of data to regulators. the cost of collecting and reporting data to meet regulatory requirements is a significant burden to both regulators and regulated firms. regulatory data collections also have significant time lag s, normally 4 to 6 weeks. one solution is to make the data reporting process better tailored to the needs of supervisors. digital regulatory reporting ( drr ) is the automation of regulatory data collection, and could potentially lead to significant improvements in both the cost and timeliness of data. the idea is based on machine readable reporting requirements that firms ’ systems could automatically interpret and satisfy via a secure regulator - firm digital link. this would allow regulators to collect data on an ad hoc basis from firms as required, in close to real time without any manual intervention at either end. that would enable amadxarif, z, brookes, j., garbarino, n., patel, r., and walczak, e ( n. d ) [ forthcoming ] all speeches are available online at www. bankofengland. co. uk / speeches supervisors to specify the data they needed to solve a particular puzzle – exposures to a particular country, for example – and transmit that data request to firms in a machine readable form. the data would then be ‘ grabbed ’ directly from firms ’ systems and sent back to supervisors automatically. the fca and bank of

used to improve the quality of underwriting by making use of nontraditional data. for example, natural language processing of annual reports and social media can give firms useful information on the quality of the credit. 5 but the increased use of ml and ai may also increase some risks to the safety and soundness of firms. implementing ml and ai at scale is likely to require considerable investments by firms in their data and technology capabilities. while in the long - run these investments could increase revenue, in the shortterm they are likely to increase costs. they will also amplify execution and operational risks. and even if firms eventually are successful in embedding new tools and techniques, these may make their businesses more complex and difficult to manage. for example, while ml models could alter banks ’ trading and retail businesses – enabling them to make better decisions more quickly – the opacity, however, of these models may also make them more difficult for humans to understand. boards, senior management and staff in firms may consequently need different skills to operate an effective oversight, risk and control environment. breslow, s., hagstroem, m., mikkelsen, d., robu, k arnold, m sirignano, j., sadwhani, a., and giesecke, k institute of international finance all speeches are available online at www. bankofengland. co. uk / speeches iii. changing the methods by which we supervise advanced analytics are also likely over time to lead to changes to the way we do our jobs as supervisors. to see how, it is perhaps easiest to go back to the basics of what prudential supervision actually is. our approach to promoting safety and soundness is based upon forward - looking judgement - based supervision, in which we identify the key risks facing firms and set supervisory strategies to mitigate them. described as a business process, it can be broken down into a number of simple steps : 1 ) rulesetting and reporting ; 2 ) analysis and monitoring ; and 3 ) setting and communicating a supervisory strategy to mitigate identified risks. each of these aspects of supervision is amenable to automation, machine learning or ai to some extent. with respect to rule setting, for example, a project is underway to use advanced analytics to understand the complexity of the pra rulebook. 6 we hope to use the results to identify ways to simplify our rules to make them easier to comply with. the pr