Research carried out at the University of Southampton into banking,
economic growth and development has made Professor Richard Werner a
trusted source of advice for economic policy-makers at the highest level,
for example for the Financial Services Authority, the Independent Banking
Commission, the International Monetary Fund and the Bank of England.
Through articles, books and many media contributions, he has promoted a
greater public understanding of economics and the financial crisis. His
credit creation analysis has also been adopted by two investment funds in
their portfolio management, leading to financial gains for investors,
outperforming the FTSE100.
Credit scoring, the process of estimating the risk of lending to
consumers, has traditionally estimated the likelihood of default over a
fixed period, usually 12 months. Research carried out at Southampton's
School of Management has led to a gradual shift by many financial
institutions in the UK and elsewhere towards an alternative method that
estimates default over any period. This approach provides accurate risk
estimates over any time period. It also allows for the inclusion in the
"scorecard" of economic conditions and the lending rates charged —
features whose absence from previous scorecards was identified as
contributing to the sub-prime mortgage crisis.
Research of Professor Brigo in the areas of credit risk, pricing models
for the valuation of
counterparty risk, and the development of accurate calibration methods of
various credit risk models
has generated significant impact both on public policy and on
practitioners and professional
services. His models were implemented and his calibration methods adopted
in the financial
industry. The significance attached to his work by the industry also
resulted in a collaboration with
the German regulator (BAFIN). Further evidence of his impact can be found
in the fact that a Court
of Law based its analysis in a financial intermediation case on Brigo's
Research by the School's Centre for Finance, Credit and
Macroeconomics (CFCM) on the monetary transmission mechanism has
been influential in improving the design, implementation and effectiveness
of the monetary policies of a number of central banks, including the Bank
of England, Banque de France and the European Central Bank.
The research has influenced changes in the way that official monetary
aggregates are measured so as to capture the impact of non-bank financial
institutions on the money supply and credit availability, and in better
understanding of how monetary policy affects different interest rates.
This in turn has allowed for improved control by central banks of their
policy targets, and for better understanding of the effects of their
monetary policies on economic activity and inflation.
The focus of this impact is the effect of the Global Vector
Autoregressive (GVAR) project on international organisations such as the
International Monetary Fund (IMF) and the European Central Bank (ECB) as
well as its use by commercial organisations such as the Economist
Intelligence Unit and the Asian Development Bank. The impact has enhanced
the tools used by the ECB for communicating its policies. It has also
allowed the IMF to demonstrate the effects of oil prices. The Economist
Intelligence Unit found it an effective framework for assessing a
wide-range of scenarios. The Asian Development Bank uses the GVAR model
for forecasting in Asia.
The School of Mathematics at Cardiff University has developed important statistical and
mathematical models for forecasting consumer buying behaviour. Enhancements to classical
models, inspired by extensively studying their statistical properties, have allowed us to exploit their
vast potential to benefit the sales and marketing strategies of manufacturing and retail
organisations. The research has been endorsed and applied by Nielsen, the #1 global market
research organisation that provides services to clients in 100 countries. Nielsen has utilised the
models to augment profits and retain their globally leading corporate position. This has led to a
US$30 million investment and been used to benefit major consumer goods manufacturers such as
Pepsi, Kraft, Unilever, Nestlé and Procter & Gamble. Therefore the impact claimed is financial.
Moreover, impact is also measurable in terms of public engagement since the work has been
disseminated at a wide range of national and international corporate events and conferences.
Beneficiaries include Tesco, Sainsbury's, GlaxoSmithKline and Mindshare WW.
Research on risk assessment of SMEs conducted at the University of
Edinburgh Business School (2005-current) in conjunction with the
international credit industry has improved understanding of SME behaviour
with a view to assisting lending bodies in their decision-making. It has
led to refinements in the process of developing commercial credit risk
models by providing valuable additional details to enhance existing
models. It has developed methodologies now used by some of the leading
lenders [text removed for publication] to cut the cost of providing
credit, thereby making more credit available to SMEs. The reach of the
work has extended across 349 credit practitioners from 38 countries.
According to the European Commission, over ninety nine per cent of
Europe's businesses are SMEs. Their success is crucial for local
enterprise, employment and taxation revenue. However, such organisations
face major obstacles to accessing additional equity that typically are not
faced by large corporations. This research has changed the way some
Italian SMEs make decisions about the relative proportion of short-and
long-term debt through adopting an optimisation model developed at
Leicester's School of Management and which is now being rolled out in
Italy and the UK. The Italian firms involved have reduced their cost of
borrowing and enhanced their reputation with banks, hence making it easier
for them to access more credit.
This case study describes impact resulting from research on assessing the
performance of credit scoring models conducted by the Consumer Credit /
Retail Banking Research Group of the Mathematics Department at Imperial
College. The group's work has influenced both high-level industry
strategies for developing scoring models, and also low-level performance
measures for which such models are developed, refined and evaluated. We
describe examples of companies or bodies that have benefitted from
improved credit scoring models, including Prescient Models (a US credit
scoring company), Experian and the US Office of the Comptroller of
Currency. The group has established a very significant reputation for a
wide range of commercially valuable work in this area — to the extent that
the group received the major Credit Collections and Risk industry
award for Contributions to the Credit Industry in 2012.
The research group investigated UK borrowers' payment protection
insurance (PPI) decision making using randomised-groups experiments and a
novel cognitive process-tracing methodology. This contributed to the
design and interpretation of a consumer survey, a key element of the
Office of Fair Trading's 2006 market study of PPI, via the involvement of
the principal investigator as consultant. The survey played a major role
in the referral of PPI to the competition commission and subsequent major
changes implemented by the FSA to the regulation of the PPI market and
associated consumer support. This has been of direct benefit to millions
of UK borrowers and has also had a major impact on the competitiveness of
the UK personal insurance market.