Speech given by James Proudman Executive Director, UK Deposit Takers Supervision
Workshop on research on bank supervision Bank of England 19 November 2018
Recognising faces comes instinctively to humans. Until fairly recently, however, it proved beyond the ability of computers. Advances in artificial intelligence (AI) – the use of a machine to simulate human behaviour – and its subset, machine learning (ML) – in which a machine teaches itself to perform tasks – are now making facial recognition software much more widely available. You might even use it to access your bank account.
Because it is so easy for us but so hard for computers, facial recognition is a good illustration of the challenges faced in developing AI. Enabling a machine to teach itself to recognise a face requires sophisticated algorithms that can learn from data. Advances in computational power and algorithmic techniques are helping machines become more human and super-human like. ML also requires lots of data from which to learn: data are the fuel that powers it – the more data used to train the algorithms, the more accurate their predictions typically become. Hence advances in AI are often associated with Big Data and the recent huge advances in the volume and variety of data available.
As the sophistication of algorithms and volume of data rise, the uses of AI in every-day life are expanding. Finance is no exception. In this speech I want to explore the impact of AI and advanced analytics more broadly, on the safety and soundness of the firms we supervise at the PRA, and how we are starting to apply such technology to the supervision of firms. In particular, I want to explore the seeming tension between the PRA’s supervisory regime that is firmly centred on human judgment, and our increasing interest and investment in automation, machine learning and artificial intelligence.
The rest of the speech can be read here
Source: Bank of England
Data extraction and integration is the second stage of a digitization process.
Increased electronification has created useable and accessible real-time and historic trade data.
The fintech uses data so institutions can assess the environment impact of their portfolios.
Working with Riskfuel will reduce reliance on slow and expensive financial models.
The platform uses AI to match experts and users and digitize the research process.