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In response to the deficiencies in bank risk management revealed following the 2008 financial crisis, one of the mandated requirements under the Basel III regulatory framework is for banks to backtest the internal models they use to price their assets and to calculate how much capital they require should a counterparty default. Qiwei Yao worked with the Quantitative Analyst — Exposure team at Barclays Bank, which is responsible for constructing the Barclays Counterpart Credit Risk (CCR) backtesting methodology. They made use of several statistical methods from Yao's research to construct the newly developed backtesting methodology which is now in operation at Barclays Bank. This puts the CCR assessment and management at Barclays in line with the Basel III regulatory capital framework.
Targeted Projection Pursuit (TPP) — developed at Northumbria University — is a novel method for interactive exploration of high-dimension data sets without loss of information. The TPP method performs better than current dimension-reduction methods since it finds projections that best approximate a target view enhanced by certain prior knowledge about the data. "Valley Care" provides a Telecare service to over 5,000 customers as part of Northumbria Healthcare NHS Foundation Trust, and delivers a core service for vulnerable and elderly people (receiving an estimated 129,000 calls per annum) that allows them to live independently and remain in their homes longer. The service informs a wider UK ageing community as part of the NHS Foundation Trust.
Applying our research enabled the managers of Valley Care to establish the volume, type and frequency of calls, identify users at high risk, and to inform the manufacturers of the equipment how to update the database software. This enabled Valley Care managers and staff to analyse the information quickly in order to plan efficiently the work of call operators and social care workers. Our study also provided knowledge about usage patterns of the technology and valuably identified clients at high risk of falls. This is the first time that mathematical and statistical analysis of data sets of this type has been done in the UK and Europe.
As a result of applying the TPP method to its Call Centre multivariate data, Valley Care has been able to transform the quality and efficiency of its service, while operating within the same budget.
Advanced technologies for data visualisation and data mining, developed in the Unit in collaboration with national and international teams, are widely applied for development of medical services. In particular, a system for canine lymphoma diagnosis and monitoring developed with [text removed for publication] has now been successfully tested using clinical data from several veterinary clinics. The risk maps produced by our technology provide early diagnosis of lymphoma several weeks before the clinical symptoms develop. [text removed for publication] has estimated the treatment test, named [text removed for publication], developed with the Unit to add [text removed for publication] to the value of their business. Institute Curie (Paris), applies this data mapping technique and the software that has been developed jointly with Leicester in clinical projects.
This case study concerns the development and subsequent uptake of the Feature Selective Validation (FSV) method for data comparisons. The method has been adopted as the core of IEEE Standard 1597.1: a `first of its kind' standard on validation of computational electromagnetics and is seeing increasingly wide adoption in industry practice where comparison of data is needed, indicating the reach and significance of this work. The technique was developed by, and under the guidance of, Dr Alistair Duffy, who has remained the world-leading researcher in the field. The first paper on the subject was published in 1997 with key papers being published in 2006.
Research by Oxford econometricians provided the basis for innovative new methods for predicting periods of potential financial stress and providing protection for investors against extreme events. During periods of financial stress, equity funds tend to sharply lose value while volatility tends to increase. Adding some long volatility exposure to a standard equity portfolio can significantly improve the tail behaviour of a portfolio. However, it is expensive to continually hold volatility contracts due to the volatility risk premium. Researchers at Man Group have applied the Oxford research to create new strategies to protect against tail risk and these are incorporated in their Tail Protect fund launched in October 2009.
Data assimilation is playing an ever increasing role in weather forecasting. Implementing four- dimensional variational data assimilation (4DVAR) is part of the long term strategy of the UK Met Office.
In this case study, an idealised 4DVAR scheme, developed by a team from the Universities of Surrey and Reading working with the UK Met Office, based on the integration of Hamiltonian dynamics and nonlinearity into data assimilation, has now been taken up by the Met Office. It is being used to evaluate options for improving operational 4DVAR. The simplicity of the scheme developed by this team has facilitated careful analyses of some generic problems with the operational model. The outcome includes direct impact on the environment and indirect impact on the economy, both through improvements in weather forecasting.
Since its launch in 2009, the mobile phone package price comparison tool Billmonitor has identified £35 million worth of savings available to the 110,000 users whose bills have been analysed. It was the first price comparison tool to be accredited by Ofcom and it has been widely praised in the media. Exploiting techniques that they had developed for applications in finance and genetics, University of Oxford researchers Chris Holmes and Nicolai Meinshausen developed the statistical algorithms underpinning the package, which uses simulation-based inference and careful statistical modelling to analyse users' phone bill data. It searches over 2.4 million available packages to identify the best mobile phone deal for each user's particular pattern of usage. Widely quoted in the press, reports in 2011 and 2012 from the Billmonitor team estimated that approximately three quarters of mobile phone customers are on the wrong tariff, with an overspend of around 40%.
This impact case is based on economic impact through improved forecasting technology. It shows how research in pattern recognition by Professor Henry Wu at the School of Electrical Engineering and Computer Science led to significantly improved accuracy of daily national gas demand forecasting by National Grid plc. The underpinning research on predicting non-linear time series began around 2002 and the resulting new prediction methodology is applied on a daily basis by National Grid plc since December 2011. The main beneficiaries from the improved accuracy (by 0.5 to 1 million cubic meters per day) are UK gas shippers, who by conservative estimates save approximately £3.5M per year. Savings made by gas shippers benefit the whole economy since they reduce the energy bills of end users.
Through a close collaboration with Ford Motor Company, simulation modelling software developed at the University of Southampton has streamlined the design of the car giant's engine production lines, increasing efficiency and delivering significant economic benefits in three key areas. Greater productivity across Ford Europe's assembly operations has generated a significant amount [exact figure removed] in direct cost savings since 2010. Automatic analysis of machine data has resulted in both a 20-fold reduction in development time, saving a large sum per year [exact figure removed], and fewer opportunities for human error that could disrupt the performance of production lines costing a large sum [exact amount removed] each to program.
A Portsmouth team has helped revolutionise how flight data from aircraft flight recorders is being analysed. This has improved the corporate performance of a leading UK company in a globally competitive market by helping it expand its business in the UK and to subsequently compete in the dynamic North American market. Historically, data was manually evaluated on a flight by flight basis. Research by the Portsmouth team means such data can now be analysed automatically by artificial intelligence (AI), saving significant man-hours, and allowing the company to diversify domestically into a related market and to expand internationally. The techniques developed were subsequently applied in a new market, enabling the new corporate partner to realise savings estimated at £100,000 p.a.