Blockchain is often discussed as if it is one single technology. But it is really a combination of several distinct features – decentralisation, distribution, cryptography, and automation – which are combined in different ways by different platforms. Some of these features may have benefits, while others may be unnecessary or even unhelpful – depending on the specific application. In this post, I consider whether and how these features may have different potential applications in financial services. Blockchain will only be truly useful in settings where one of more of these features solves a problem that existing technologies cannot.
Nicola Medicoff from St Paul’s Girls School, Hammersmith is the runner up in the Bank of England/Financial Times schools blogging competition. In her post, she looks at how fintech might reshape the banking industry…
Six years after setting up shop in London, ride-hailing app Uber has a fleet of 40,000 drivers doing battle with Black cabs, upsetting an industry that has seen little change since Hackney carriages started in the 1650s. Banks are bracing themselves for a similar assault, in their case from small fintech start-ups and large technology groups. Are the banks’ fears justified?
Peer to Peer (P2P) lending is a hot topic at Fintech events and has received a lot of attention from academia, journalists, various international bodies and regulators. Following the Financial Crisis, P2P platforms saw an opportunity to fill a gap in the market by offering finance to customers and businesses struggling to get loans from banks. Whilst some argue they will one day revolutionise the whole banking landscape, many platforms have not yet turned a profit. So before asking if they are the future, we should first ask if they have a future at all. Problems such as a higher cost of funds, or limited ability to scale the business, may mean the only viable path is to become more like traditional banks.
Smartphone apps and newsfeeds are designed to constantly grab our attention. And research suggests we’re distracted nearly 50% of the time. Could this be weighing down on productivity? And why is the crisis of attention particularly concerning in the context of the rise of AI and the need, therefore, to cultivate distinctively human qualities?
Rapid advances in analytical modelling and information processing capabilities, particularly in machine learning (ML) and artificial intelligence (AI), combined with ever more granular data are currently transforming many aspects of everyday life and work. In this blog post we give a brief overview of basic concepts of ML and potential applications at central banks based on our research. We demonstrate how an artificial neural network (NN) can be used for inflation forecasting which lies at the heart of modern central banking. We show how its structure can help to understand model reactions. The NN generally outperforms more conventional models. However, it struggles to cope with the unseen post-crises situation which highlights the care needed when considering new modelling approaches.
The topics of central bank digital currency (CBDC) and distributed ledger technology (DLT) are often implicitly linked. The genesis of recent interest in CBDC was the emergence of private digital currencies, like Bitcoin, which often leads to certain assumptions about the way a CBDC might be implemented – i.e. that it would also need to use a form of blockchain or DLT. But would a CBDC really need to use DLT? In this post I explain that it may not be necessary to use DLT for a CBDC, but I also consider some of the reasons why it could still be desirable.
This post highlights some of the possible economic implications of the so-called “Fourth Industrial Revolution” — whereby the use of new technologies and artificial intelligence (AI) threatens to transform entire industries and sectors. Some economists have argued that, like past technical change, this will not create large-scale unemployment, as labour gets reallocated. However, many technologists are less optimistic about the employment implications of AI. In this blog post we argue that the potential for simultaneous and rapid disruption, coupled with the breadth of human functions that AI might replicate, may have profound implications for labour markets. We conclude that economists should seriously consider the possibility that millions of people may be at risk of unemployment, should these technologies be widely adopted.