The University of Southampton
Southampton Business School

Professor Christophe Mues 

Professor of Data Science and Information Systems

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Prior to my appointment at the University of Southampton, I was employed as a researcher and teaching assistant at KU Leuven (Belgium), where I obtained the degree of Doctor in Applied Economics. In September 2004, I joined the Southampton Business School, where I am currently Programme Leader of the MSc Knowledge and Information Systems Management (KISM) and teach on a variety of subjects in information systems and analytics.

A unifying theme in my research and collaborations with industry is to identify new credit risk modelling and predictive analytics challenges and find novel solutions to tackle them. Some of my recent work for example has been to develop and validate different methods to predict Loss Given Default (LGD), i.e. the proportion of a loan that a lender is unable to recover if the borrower defaults. More in general I have investigated a variety of (statistical or other) methods to model consumer credit risk in the context of Basel II and III, customer churn, etc. In these different research areas, I cooperate regularly with my colleagues Lyn Thomas, Bart Baesens, and Meko So.

I am always happy to discuss possible research collaborations that fit in with these interests or set up data exchanges with potential industry partners to try and answer new research questions: just drop me an email. I am also currently welcoming PhD applications in these areas, provided that the applicant has a strong quantitative background (e.g. statistics — regardless of the application domain! —, quantitative risk modelling or finance, applied mathematics, machine learning, etc.), coupled with a healthy interest in applying those skills to business analytics problems. Again, send me an email if you are interested.

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Research interests

 

My research interests span areas such as knowledge engineering and business intelligence, where I have introduced and investigated the use of decision tables and (binary) decision diagram (BDD) techniques in a variety of problem contexts such as the verification and validation of knowledge-based systems, business rule modelling, and knowledge discovery and data mining. In addition, I have published extensively on novel data mining techniques (e.g. neural network rule extraction) and applied them to problems in credit risk modelling, customer relationship modelling, and the empirical software engineering domain. Most importantly perhaps, I have developed an extensive research agenda in credit risk modelling and new challenges related to Basel II and III such as Loss Given Default (LGD) estimation in consumer lending.

My publication record to date includes 30 refereed journal papers, many of which are in 3*+ ABS-rated management journals. Some of this work has been previously funded by EPSRC, SAS-UK, or Fair Isaac.

 

 

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Professor Christophe Mues
Southampton Business School, University of Southampton, Highfield, Southampton SO17 1BJ, UK

Room Number:2/3009

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