Abstract

Machine learning for predicting and mitigating operational risk

In the wake of the global financial crisis in 2007-2008, the Basel II and III global regulatory accords, amongst others, stipulate the holding of so-called regulatory capital to off-set potential losses.

The risk of potential loss needs to be estimated by statistical models. A type of risk newly emphasised in Basel II and II is operational risk - concerning losses caused by fraud, bad practices, disasters, or process failures, or, more generally, human and environment/infrastructure factors.

The challenge is to develop accurate models for predicting operational risk, with the goal of preventing avoidable risks and mitigating unavoidable ones through sufficient regulatory capital.

Citation information

Data Study Group team. (2019). Data Study Group Final Report: Global bank. http://doi.org/10.5281/zenodo.2557809

Additional information

Jonathan Sadeghi, University of Liverpool
David Berman, Queen Mary University of London
Shaoxiong Hu, Queen Mary University of London
Nam Pho, Harvard Medical School
Marc Williams, UCL
Diego Arenas, University of St Andrews.
Alvaro Cabrejas Egea, University of Warwick
Fangfang Niu
Medb Corcoran, 
Accenture
Simon Frost, University of Cambridge
Lukas Danev
Kumutha Swampillai,
Accenture