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Christophe Mues is Professor of Data Science and Information Systems, specialising in credit scoring and other applications of analytics in consumer lending.
Research
Research groups
Research interests
- Much of his research involves applications of predictive and prescriptive analytics in the area of credit scoring and consumer credit risk modelling. For example, he has researched advanced statistical or machine learning methods to predict Probability of Default (PD), Loss Given Default (LGD), i.e. the proportion of a loan that a lender is unable to recover if the borrower defaults, credit card balance at default, time to default (using survival analysis), and loan profitability.
Current research
Professor Mues is currently researching a variety of topics relating to consumer or SME credit risk modelling, including but not restricted to the use of deep learning techniques to incorporate non-traditional data sources, the transparency and fairness of credit scoring models, and debt collection.
Publications
Teaching
Professor Mues teaches a variety of subjects relating to information systems and business analytics.
Biography
Previously, he was employed as a researcher at KU Leuven, Belgium, where he obtained the degree of Doctor in Applied Economics. Having joined the School in September 2004, he now leads the Information Systems & Business Analytics section within the Department of Decision Analytics and Risk. He is a member of the organising committee for the main conference in the credit scoring area (the biennial Credit Scoring and Credit Control conference).