. com / article / amazon - ai - the - smart - persons - guide / ; and fei - fei li and jia li, “ cloud automl : making ai accessible to every business, ” the keyword ( google blog ), january 17, 2018, https : / / www. blog. google / topics / google - cloud / cloud - automl - making - ai - accessible - every - business /. sintef, “ big data, for better or worse : 90 % of world ’ s data generated over last two years, ” sciencedaily. com, may 22, 2013, https : / / www. sciencedaily. com / releases / 2013 / 05 / 130522085217. htm ; and ibm, “ 10 key marketing trends for 2017 ” ( 2017 ) ( on file with author ). executive office of the president, preparing for the future of artificial intelligence. - 3so it is no surprise that many financial services firms are devoting so much money, attention, and time to developing and using ai approaches. broadly, there is particular interest in at least five capabilities. 8 first, firms view ai approaches as potentially having superior ability for pattern recognition, such as identifying relationships among variables that are not intuitive or not revealed by more traditional modeling. second, firms see potential cost efficiencies where ai approaches may be able to arrive at outcomes more cheaply with no reduction in performance. third, ai approaches might have greater accuracy in processing because of their greater automation compared to approaches that have more human input and higher “ operator error. ” fourth, firms may see better predictive power with ai compared to more traditional approaches - - for instance, in improving investment performance or expanding credit access. finally, ai approaches are better than conventional approaches at accommodating very large and less - structured data sets and processing those data more efficiently and effectively. some machine learning approaches can be “ let loose ” on data sets to identify patterns or develop predictions without the need to specify a functional form ex ante. what do those capabilities mean in terms of how we bank? the financial stability board highlighted four areas where ai could impact banking. 9 first, customer - facing uses could combine expanded consumer data sets with new algorithms to assess credit quality or price insurance policies. and chatbots could provide help and even financial advice to consumers, ai tools are also likely to be useful for central banks and regulators in their responsibilities for supervision, financial stability, and monetary

outside vendor. it also describes ways that firms can provide oversight and monitoring throughout the relationship with the vendor, and considerations see, e. g., ffiec outsourcing technology services booklet ( june 2004 ), http : / / ithandbook. ffiec. gov / itbooklets / outsourcing - technology - services. aspx ; and board of governors, “ guidance on managing outsourcing risk, ” supervision and regulation letter sr 13 - 19 / consumer affairs letter ca 13 - 21 ( december 5, 2013 ), https : / / www. federalreserve. gov / supervisionreg / srletters / sr1319. htm. - 7about business continuity and contingencies for a firm to consider before the termination of any such relationship. third, it is important to emphasize that guidance has to be read in the context of the relative risk and importance of the specific use - case in question. we have long taken a riskfocused supervisory approach - - the level of scrutiny should be commensurate with the potential risk posed by the approach, tool, model, or process used. 16 that principle also applies generally to the attention that supervised firms devote to the different approaches they use : firms should apply more care and caution to a tool they use for major decisions or that could have a material impact on consumers, compliance, or safety and soundness. for its part, ai is likely to present some challenges in the areas of opacity and explainability. recognizing there are likely to be circumstances when using an ai tool is beneficial, even though it may be unexplainable or opaque, the ai tool should be subject to appropriate controls, as with any other tool or process, including how the ai tool is used in practice and not just how it is built. this is especially true for any new application that has not been fully tested in a variety of conditions. given the large data sets involved with most ai approaches, it is vital to have controls around the various aspects of data - - including data quality as well as data suitability. just as with conventional models, problems with the input data can lead to cascading problems down the line. accordingly, we would expect firms to apply robust analysis and prudent risk management and controls to ai tools, as they do in other areas, as well as to monitor potential changes and ongoing developments. board of governors, “ risk - focused safety and soundness examinations