This has naturally put us, along with other central banks and supervisors on alert for further signs of strain and the attendant risks of dysfunction. UK financial assets saw significant repricing since the start of this week, particularly affecting long-dated UK government debt. Were dysfunction in this market to continue or worsen, there would be a material risk to UK financial stability. This would lead to an unwarranted tightening of financing conditions and a reduction of the flow of credit to the real economy. On 28 September, yesterday, the Bank’s Financial Policy Committee, which I’m a member of, noted the risks to UK financial stability from dysfunction in the gilt market. It recommended that action be taken and welcomed the Bank’s plans for temporary and targeted purchases in the gilt market on financial stability grounds at an urgent pace. These operations have started, with the first one carried out yesterday afternoon. These purchases will be strictly time limited until 14 October. They are intended to tackle a specific problem in the long-dated government bond market. The purchases will be unwound in a smooth and orderly fashion once risks to market functioning are judged to have subsided. The Bank’s MI function has informed these actions by providing the Bank’s policy committees with detailed, real-time information on market functionality and tradeability.
This would reduce the number of “false positive” rates on transactions and reduce the costs of checks. This is one example of how a common approach to using payment data globally, harmonising our implementation of payment message standards and tackling potential barriers to sharing payment data across jurisdictions could reap significant benefits. The G20 roadmap for cross-border payments highlights key actions for jurisdictions, system operators and the wider financial system and payments industry to undertake and I would encourage all of us who are involved to redouble our efforts[9] . Through that collective effort we will make payments, quicker, smoother and cheaper for all. The benefits of richer, more structured data, including payments data, will only be fully realised if data users are able to access and extract value from the ever increasing pool of data they now have access to. The financial sector has been at the forefront of using artificial intelligence techniques such as machine learning to extract value from the explosion of timely data they now have access to, as the Bank’s surveys have found. [10] It’s obvious why as adoptions of technologies like machine learning have the potential to make financial services and markets more efficient, accessible and tailored to consumer needs[11]. I don’t have time to do anything like justice to this subject today but before I leave it let me just return to the example of fraud detection and anti-money laundering. Here machine learning is particularly useful for card authorisations that flow through the payments chain, very much in realtime.