From a financial management point of view, large investors may give priority to companies with a low risk profile, in order to bring back their own asset portfolios to levels of volatility matching their requirements. Therefore listed companies will have an incentive to adjust their risk profile and avoid more hazardous –which may also be more innovative– investment projects. Therefore, “entrepeneurship” may be curbed and the growth potential may be influenced adversely. Another impact of volatility on financial management is related to recourse to hedging, namely derivative products. As I mentioned earlier, derivatives are a driving force behind mature financial markets, and their strong development in the last two decades is merely consequence of the benefits they have generated. More precisely, derivatives and related financial innovations have enabled companies to transfer the impact of interest rate and currency fluctuations to financial institutions or investors that were ready to manage it, whereby facilitating international trade or project financing. Meanwhile, the pricing of derivatives is closely linked to volatility. As volatility is indeed difficult to predict, market practitioners have developed models, which are constantly being refined. Over time, market participants, –especially banks,– have become more competent in managing risk, while being left that residual risks which are more complex and harder to control. These consequences would not be a major concern, if the episodes of excessive volatility were only accidental. Peaks of volatility clearly reflect macroeconomic imbalances, as soon as market participants take into account the relevant information.

Large established firms seem to be most advanced in deployment. There is some reliance on external providers at various levels, ranging from providing infrastructure, the programming environment, up to specific solutions. Approaches to testing and explaining AI are being developed and, perhaps unsurprisingly, there is some heterogeneity in techniques and tools. Firms said that ML applications are embedded in their existing risk  6  McKinsey (2018) ‘Adoption of AI advances, but foundational barriers remain’ 4  All speeches are available online at www.bankofengland.co.uk/publications/Pages/speeches/default.aspx  4  frameworks. But many say that new approaches to model validation (which include AI explainability techniques) are needed in the future. Of the firms regulated by the Bank of England that responded to the survey, 57 per cent reported that they are using AI applications in risk management and compliance areas, including anti-fraud and anti-money laundering applications. In customer engagement, 39 per cent of firms are using AI applications, 25 per cent in sales and trading, 23 per cent in investment banking, and 20 per cent in non-life insurance. By and large, firms reported that, properly used, AI and ML would lower risks - most notably, for example, in anti-money laundering, KYC and retail credit risk assessment. But some firms acknowledged that, incorrectly used, AI and ML techniques could give rise to new, complex risk types - and that could imply new challenges for boards and management. Challenges of AI and ML for boards  Let me suggest that there are three challenges for boards and management.