(Adams, Anagnostopoulos, Bellotti, Bakoben, Hand, Heard, Gandy, Missaoui, Veraart)

The aims and objectives of the Statistical Methods in Retail Financial Services Research Group are:

  • To apply statistical methods in the financial sector of industry; and

  • To develop new statistical methodology arising from the novel challenges presented by these applications.

It is very apparent that the level of sophistication of the methods and tools used by the banks to control their retail credit operations is increasing rapidly, and we aim to continue to be in the vanguard of this development. Our research involves all aspects of mathematics and statistics relating to the retail finance industry, including fraud detection, portfolio modelling, default correlation and risk management.

We have numerous links with organisations within the retail banking sector, and are always involved in discussions with banks and other financial bodies about possible collaborations or research sponsorships.  Previous projects have been sponsored by Fair Isaac, Link Financial, and other bodies.

Specific areas of research interest within the Statistics for Retail Finance group:

  • Evaluating scorecards (Hand, Anagnostopoullos, Adams)
  • Fraud detection (Hand, Adams, Heard)
  • Inclusion of macroeconomic conditions on credit risk (Bellotti)
  • Use of survival and panel models for credit risk (Bellotti)
  • Applications of machine learning in credit risk modelling (Bellotti)
  • Stress Testing (Bellotti)
  • Modelling Loss Given Default (Bellotti)
  • Applications of graph mining to portfolio credit risk and fraud detection (Missaoui)

The group is part of the Quantitative Financial Risk Management Centre (QFRMC). This is a research consortium run from the Department of Mathematics at Imperial College, the School of Management at the University of Southampton, and the University of Edinburgh Business School. It was established by the Engineering and Physical Sciences Research Council, with additional funding from the Economic and Social Research Council and the Institute of Actuaries. It carries out research and organises conferences and other meetings addressing issues in the retail financial services sector, in collaboration with banks, credit agencies, and other bodies


  • Contributions to the Credit Industry Award, Credit Collections and Risk 2012 annual industry awards

Selected Publications (in chronological order):

Invited Talks / Keynote Presentations:

  • Bellotti (2013) CFE 2013, 7th CSDA International Conference on Computational and Financial Econometrics 
  • Bellotti (2013) Royal Statistical Society Workshop: Advanced Statistical Methods in Credit Risk
  • Bellotti (2013) 8th Annual Forum on Retail Credit Risk, London
  • Bellotti (2013) National Association of Data Protection Officers
  • Hand (2012) "Big data: risks, opportunities, and challenges" Demographics User Forum conference on Retail issues, big data, and research.
  • Hand (2012) " Discriminating or distinguishing? Legalities and moralities in scorecard construction" CCR-interactive, London
  • Hand (2012) "Big bang, big data, big computers. Opportunities and challenges in modelling and anomaly detection", Paris 
  • Hand (2012) Keynote address, RSS Risk in Business: the business of risk, Telford
  • Hand (2012) "Innovation in customer decisioning to add business value", Infoline Retail Credit Risk conference

Impact / Industrial Collaborations / Consultancy:

The Statistics in Retail Finance Research Group are active collaborators with the finance industry and have been involved in many successful consultancy projects with several financial institutions over the past thirty years. We usually work through Imperial Consultants (ICON). Some types of consultancy projects we have undertaken are:

  • Credit risk model development
  • Model validation and evaluation
  • Data network analysis
  • Fraud detection models

We regularly organize workshops targeted to practitioners in retail finance as well as the academic community. In the 2012/13 academic year we have hosted workshops onModel Risk and Big Data.


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