The total amount of our loans more than tripled, climbing from CHF 31 billion at the end of 2007 to CHF 101 billion at the end of 2008. In parallel, as a way of enabling banks to place their liquid means with the SNB in a form other than sight deposits, and of introducing a tool which would allow us to absorb large amounts of liquidity if need be, we issued SNB Bills for the first time on 20 October 2008. By the end of the year, the sum of SNB Bills issued had reached CHF 24.4 billion. Active Swiss franc liquidity management enabled us to hold the three-month Swiss franc Libor at the level targeted by our monetary policy. I shall return to this point later. Consolidating UBS In autumn 2007, the National Bank realised that Switzerland’s two big banks would be particularly badly affected by the financial market turbulence. At that point, we intensified our contacts with the Swiss Federal Banking Commission (SFBC) (now the Swiss Financial Market Supervisory Authority FINMA) in order to ensure ongoing monitoring of the situation at these two institutions. From spring 2008 on, in view of UBS’s growing vulnerability, we drew up a number of solutions to protect it from the consequences of a large volume of illiquid assets on its balance sheet. In autumn, after the collapse of Lehman Brothers in the US led to liquidity drying up completely on the interbank market, UBS encountered increasing refinancing difficulties and was facing a substantial loss of funds.
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 regulatory landscape is somewhat fragmented when it comes to AI, with different pieces of regulation applying to different aspects of the AI pipeline, from data through model risk to governance. Policy must strike a balance between high-level principles and a more rules-based approach. We also need to future-proof our policy initiatives in a fast-changing field. With all that in mind, and in order to further our objective of promoting the safe adoption of AI in financial services, the Bank of England, along with the Financial Conduct Authority, is today launching the Artificial Intelligence Public-Private Forum.