11.21.2018

Cyborg Supervision

Speech given by James Proudman Executive Director, UK Deposit Takers Supervision

Workshop on research on bank supervision Bank of England 19 November 2018

I. Introduction

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

 

Related articles

  1. NeoXam helps asset managers consolidate data from different providers.

  2. Proper governance is not a roadblock to development.

  3. OPINION: Artificial, Yes. Intelligent? Maybe.

    SynFinance uses AI to identify investors most likely to participate in a syndicated loan.

  4. Qplum turns to HFT data to meet its data needs.

  5. Success relies on buy in from the rank and file, not just the IT department.