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Phone:: (023) 8059 2561
Email:: C.Mues@soton.ac.uk

Professor Christophe Mues

Professor of Data Science and Information Systems

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.

Research

Publications

Contact

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.

Department of Decision Analytics and Risk

Research member

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Articles

So, M., Mues, C., & Thomas, L. C. (2018). Debtor level collection operations using Bayesian dynamic programming. Journal of the Operational Research Society.
Fitzpatrick, T., & Mues, C. (2016). An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market. European Journal of Operational Research, 249(2), 427-439. DOI: 10.1016/j.ejor.2015.09.014
Tong, E., Mues, C., Brown, I., & Thomas, L. C. (2016). Exposure at default models with and without the credit conversion factor. European Journal of Operational Research, 1-35. DOI: 10.1016/j.ejor.2016.01.054
Crook, J., Bellotti, T., & Mues, C. (2015). Editorial. European Journal of Operational Research, 1-2. DOI: 10.1016/j.ejor.2015.10.009
Loterman, G., & Mues, C. (2015). Learning algorithm selection for comprehensible regression analysis using datasetoids. Intelligent Data Analysis, 19(5), 1019-1034. DOI: 10.3233/IDA-150756
Thomas, L. C., Matuszyk, A., So, M. C., Mues, C., & Jezewska, A. (2015). Modelling repayment patterns in the collections process for unsecured consumer debt: a case study. European Journal of Operational Research. DOI: 10.1016/j.ejor.2015.09.013
Loterman, G., Debruyne, M., Vanden Branden, K., Van Gestel, T., & Mues, C. (2014). A proposed framework for backtesting loss given default models. Journal of Risk Model Validation, 8(1), 69-90.
Bijak, K., Thomas, L. C., & Mues, C. (2014). Dynamic affordability assessment: predicting an applicant’s ability to repay over the life of the loan. Journal of Credit Risk, 10(1), 3-32. DOI: 10.21314/JCR.2014.171
So, M. C., Thomas, L. C., Seow, H-V., & Mues, C. (2014). Using a transactor/revolver scorecard to make credit and pricing decisions. Decision Support Systems, 59, 143-151. DOI: 10.1016/j.dss.2013.11.002
Tong, E. N. C., Mues, C., & Thomas, L. C. (2013). A zero-adjusted gamma model for mortgage loan loss given default. International Journal of Forecasting, 29(4), 548-562. DOI: 10.1016/j.ijforecast.2013.03.003
Leow, M., Mues, C., & Thomas, L. C. (2013). The economy and loss given default: evidence from two UK retail lending data sets. Journal of the Operational Research Society, 65, 363-375. DOI: 10.1057/jors.2013.120
Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39(3), 3446-3453. DOI: 10.1016/j.eswa.2011.09.033
Loterman, G., Brown, I., Martens, D., Mues, C., & Baesens, B. (2012). Benchmarking regression algorithms for loss given default modeling. International Journal of Forecasting, 28(1), 161-170. DOI: 10.1016/j.ijforecast.2011.01.006
Tong, E. N. C., Mues, C., & Thomas, L. C. (2012). Mixture cure models in credit scoring: if and when borrowers default. European Journal of Operational Research, 218(1), 132-139. DOI: 10.1016/j.ejor.2011.10.007
Leow, M., & Mues, C. (2012). Predicting loss given default (LGD) for residential mortgage loans: a two-stage model and empirical evidence for UK bank data. International Journal of Forecasting, 28(1), 183-195. DOI: 10.1016/j.ijforecast.2011.01.010
Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., & Baesens, B. (2011). An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems, 51(1), 141-154. DOI: 10.1016/j.dss.2010.12.003
Verbeke, W., Martens, D., Mues, C., & Baesens, B. (2011). Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications, 38(3), 2354-2364. DOI: 10.1016/j.eswa.2010.08.023
Martens, D., Vanhoutte, C., De Winne, S., Baesens, B., Sels, L., & Mues, C. (2011). Identifying financially successful start-up profiles with data mining. Expert Systems with Applications, 38(5), 5794-5800. DOI: 10.1016/j.eswa.2010.10.052
Lima, E., Mues, C., & Baesens, B. (2011). Monitoring and backtesting churn models. Expert Systems with Applications, 38(1), 975-982. DOI: 10.1016/j.eswa.2010.07.091
Setiono, R., Baesens, B., & Mues, C. (2011). Rule extraction from minimal neural networks for credit card screening. International Journal of Neural Systems, 21(4), 265-276. DOI: 10.1142/S0129065711002821
Thomas, L. C., Mues, C., & Matuszyk, A. (2010). Modelling LGD for unsecured personal loans: decision tree approach. Journal of the Operational Research Society, 61, 393-398. DOI: 10.1057/jors.2009.67
De Almeida Filho, A., Mues, C., & Thomas, L. C. (2010). Optimizing the collections process in consumer credit. Production and Operations Management, 19(6), 698-708. DOI: 10.1111/j.1937-5956.2010.01152.x
Baesens, B., Mues, C., Martens, D., & Vanthienen, J. (2009). 50 Years of data mining and OR: upcoming trends and challenges. Journal of the Operational Research Society, 60(Supplement 1), S16-S23. DOI: 10.1057/jors.2008.171
Setiono, R., Baesens, B., & Mues, C. (2009). A note on knowledge discovery using neural networks and its application to credit card screening. European Journal of Operational Research, 192(1), 326-332. DOI: 10.1016/j.ejor.2007.09.022
Lima, E., Mues, C., & Baesens, B. (2009). Domain knowledge integration in data mining using decision tables: case studies in churn prediction. Journal of the Operational Research Society, 60(8), 1096-1106. DOI: 10.1057/jors.2008.161
Lessmann, S., Baesens, B., Mues, C., & Pietsch, S. (2008). Benchmarking classification models for software defect prediction: a proposed framework and novel findings. IEEE Transactions on Software Engineering, 34(4), 485-496. DOI: 10.1109/TSE.2008.35
Vandecruys, O., Martens, D., Baesens, B., Mues, C., De Backer, M., & Haesen, R. (2008). Mining software repositories for comprehensible software fault prediction models. Journal of Systems and Software, 81(5), 823-839. DOI: 10.1016/j.jss.2007.07.034
Setiono, R., Baesens, B., & Mues, C. (2008). Recursive neural network rule extraction for data with mixed attributes. IEEE Transactions on Neural Networks, 19(2), 299-307. DOI: 10.1109/TNN.2007.908641
Mues, C., & Tutunovic, M. (2008). Uz Manje Rizika Do Vise Kapitala. Bankar Magazin, 86-87.
Hoffmann, F., Baesens, B., Mues, C., & Vanthienen, J. (2007). Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms. European Journal of Operational Research, 177(1), 540-555. DOI: 10.1016/j.ejor.2005.09.044
Baesens, B., Mues, C., Van Gestel, T., & Vanthienen, J. (2006). Preface. Special issue on intelligent information systems for financial engineering. Expert Systems with Applications, 30(3), 413-414. DOI: 10.1016/j.eswa.2005.10.001
Mues, C., Baesens, B., Files, C. M., & Vanthienen, J. (2004). Decision diagrams in machine learning: an empirical study on real-life credit-risk data. Expert Systems with Applications, 27(2), 257-264. DOI: 10.1016/j.eswa.2004.02.001
Mues, C., & Vanthienen, J. (2004). Efficient rule base verification using binary decision diagrams. Lecture Notes in Computer Science, 3180(Jan 2004), 445-454. DOI: 10.1007/b99664
Baesens, B., Mues, C., & Vanthienen, J. (2003). Knowledge discovery in data: van academische denkoefening naar bedrijfsrelevante praktijk. Informatie: Maandblad voor de informatievoorziening, 45(1), 30-35.
Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003). Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science, 49(3), 312-329. DOI: 10.1287/mnsc.49.3.312.12739
Martens, J., Wets, G., Vanthienen, J., & Mues, C. (2000). Improving a neuro-fuzzy classifier using exploratory factor analysis. International Journal of Intelligent Systems, 15(8), 785-800. DOI: 10.1002/1098-111X(200008)15:8<785::AID-INT6>3.0.CO;2-L
Vanthienen, J., Mues, C., Wets, G., & Delaere, K. (1998). A tool-supported approach to inter-tabular verification. Expert Systems with Applications, 15(3-4), 277-285. DOI: 10.1016/S0957-4174(98)00047-5
Vanthienen, J., Mues, C., & Aerts, A. (1998). An illustration of verification and validation in the modelling phase of KBS development. Data and Knowledge Engineering, 27(3), 337-352. DOI: 10.1016/S0169-023X(98)80003-7
Martens, J., Wets, G., Vanthienen, J., & Mues, C. (1998). An initial comparison of a fuzzy neural classifier and a decision tree based classifier. Expert Systems with Applications, 15(3-4), 375-381. DOI: 10.1016/S0957-4174(98)00057-8

Book

Mues, C., & J. Vanthienen (2002). On the use of decision tables and diagrams in knowledge modelling and verification. Katholieke Universiteit Leuven. Department of Applied Economic Sciences.

Book Chapters

Goedertier, S., Mues, C., & Vanthienen, J. (2007). Specifying process-aware access control rules in SBVR. In Advances in Rule Interchange and Applications (pp. 39-52). (Lecture Notes in Computer Science; Vol. 4824). Berlin. Germany: Springer. DOI: 10.1007/978-3-540-75975-1
Martens, D., De Backer, M., Haesen, R., Baesens, B., Mues, C., & Vanthienen, J. (2006). Ant-based approach to the knowledge fusion problem. In Ant Colony Optimization and Swarm Intelligence (pp. 84-95). (Lecture Notes in Computer Science; Vol. 4150/2006). Berlin, Germany: Springer. DOI: 10.1007/11839088_8
Baesens, B., Setiono, R., Mues, C., Viaene, S., & Vanthienen, J. (2001). Building intelligent credit-risk evaluation systems using neural network rule extraction and decision tables. In J. Vandenbulcke, & M. Snoeck (Eds.), New Directions in Software Engineering (pp. 121-133). Leuven, Belgium: Leuven University Press.
Vanthienen, J., Mues, C., & Aerts, A. (2001). Integrating verification aspects into the modelling of KBS: an example using decision tables. In A. J. Baborski, R. F. Bonner, & M. L. Owoc (Eds.), Knowledge Acquisition and Distributed Learning in Resolving Managerial Issues (pp. 113-125). Sweden: Mälardalen University Press.
Mues, C., Baesens, B., Huysmans, J., & Vanthienen, J. (1970). Comprehensible credit-scoring knowledge visualization using decision tables and diagrams. In Enterprise Information Systems VI The Netherlands: Kluwer.

Conferences

Mues, C., Calabrese, R., & So, M. (2018). Predicting credit card debt recovery rates: an empirical study using generalised additive models. In 29th European Conference on Operational Research
So, M. C., Thomas, L. C., Mues, C., & Seow, H-V. (2015). Estimating the profitability of transactors and revolvers. Paper presented at Credit Scoring and Credit Control XIV, United Kingdom.
Mues, C., Thomas, L. C., & So, M. C. (2014). Loan recovery modelling: challenges for predictive and prescriptive analytics. Paper presented at 20th Conference of the International federation of Operational Research Societies, Spain.
Bijak, K., Thomas, L. C., & Mues, C. (2013). Assessing affordability using random effects models of income and consumption growth. Credit Scoring and Credit Control XIII Conference, United Kingdom.
So, M. C., De Almeida Filho, A., Mues, C., & Thomas, L. C. (2013). Modelling the collections policy. Paper presented at Credit Scoring and Credit Control XIII, United Kingdom.
Tong, E., Mues, C., & Thomas, L. C. (2011). A zero-adjusted gamma model for estimating loss given default on residential mortgage loans. Paper presented at Credit Scoring and Credit Control XII conference, United Kingdom.
Leow, M., Mues, C., & Thomas, L. C. (2011). Competing risks survival model for mortgage loans with simulated loss distributions. Paper presented at Credit Scoring and Credit Control XII conference, United Kingdom.
Leow, M., Mues, C., & Thomas, L. C. (2010). Competing risks survival model for residential mortgage loans. Paper presented at 24th European Conference on Operational Research (EURO XXIV), Portugal.
Tong, E., Mues, C., & Thomas, L. C. (2010). Mixture cure models in consumer credit risk. Paper presented at 24th European Conference on Operational Research (EURO XXIV), Portugal.
Brown, I., Mues, C., & Thomas, L. C. (2010). Regression model development for exposure at default (EAD). Paper presented at 24th European Conference on Operational Research (EURO XXIV), Portugal.
Dejaeger, K., Verbeke, W., Huysmans, J., Mues, C., Vanthienen, J., & Baesens, B. (2010). Rule based predictive models, decision table and tree: an empirical evaluation on comprehensibility. Paper presented at 24th European Conference on Operational Research (EURO XXIV), Portugal.
Brown, I., & Mues, C. (2009). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Paper presented at 23rd European Conference on Operational Research, Germany.
Loterman, G., Brown, I., Martens, D., Mues, C., & Baesens, B. (2009). Benchmarking state-of-the-art regression algorithms for loss given default modelling. Paper presented at 11th Credit Scoring and Credit Control Conference (CSCCXI), United Kingdom.
Leow, M., Mues, C., & Thomas, L. C. (2009). Loss given default (LGD) modelling for mortgage loans. Paper presented at 11th Premier Credit Scoring Conference, Credit Scoring and Credit Control (CSCC XI), United Kingdom.
Leow, M., Mues, C., & Thomas, L. C. (2009). Loss given default modelling for mortgage loans. Paper presented at 23rd European Conference on Operational Research, Germany.
De Almeida Filho, A., Mues, C., & Thomas, L. C. (2008). Collection process and behavioural analysis through dynamic programming models. Paper presented at OR50 Annual Conference, United Kingdom.
Lima, E., Mues, C., & Baesens, B. (2008). Integrating domain knowledge into data mining: A customer focused analysis. Paper presented at OR50 Annual Conference, United Kingdom.
Thomas, L. C., Matuszyk, A., De Almeida Filho, A., & Mues, C. (2008). Modelling the collection process for unsecured consumer loans. Paper presented at 18th Triennial Conference of the International Federation of Operational Research Societies Conference (IFORS2008), .
Mues, C., Huysmans, J., Baesens, B., & Vanthienen, J. (2007). An empirical investigation into the interpretability of data mining models based on decision trees, tables and rules. Paper presented at 22nd European Conference on Operational Research (EURO XXII), .
Lima, E., Baesens, B., & Mues, C. (2007). Customer lifetime value and churn management: a survey and data mining approach. Paper presented at 22nd European Conference on Operational Research EURO XXII, .
Matuszyk, A., Thomas, L. C., & Mues, C. (2007). Loss given default process modelling. Paper presented at Quantative Financial Risk Management Centre Symposium on Risk Management in the Retail Financial Services Sector, United Kingdom.
Thomas, L. C., Mues, C., & Matuszyk, A. (2007). Modelling LGD for unsecured personal loans: decision tree approach. Paper presented at 10th Credit Scoring & Credit Control Conference (CSCC X), United Kingdom.
Goedertier, S., Mues, C., & Vanthienen, J. (2007). Specifying process-aware access control rules in SBVR. Paper presented at International RuleML Symposium on Rule Interchange and Applications (RuleML2007), .
Martens, D., De Backer, M., Haesen, R., Baesens, B., Mues, C., & Vanthienen, J. (2006). Ant-based approach to the knowledge fusion problem. Paper presented at 5th International Workshop on Ant Colony Optimization and Swarm Intelligence (ANTS2006), .
Martens, D., Baesens, B., Mues, C., Seow, H-V., Shahi, R., & Vanthienen, J. (2006). Building acceptable classifiers with ants. Paper presented at 21st European Conference on Operational Research: EURO XXI, .
Mues, C. (2006). Data mining: emerging trends, challenges and applications. OR48 Annual Conference, .
Vanthienen, J., Goedertier, S., & Mues, C. (2006). Experiences with Modeling and Verification of Regulations. Paper presented at International Workshop on Regulations Modelling and their Validation & Verification (REMO2V '06), in conjunction with the 18th Conference on Advanced Information System Engineering (CAiSE '06), .
Mues, C., Baesens, B., Martens, D., Seow, H-V., & Shahi, R. (2006). Knowledge Discovery and Implementation using Neural Network Rule Extraction and Decision Tables. Paper presented at 21st European Conference on Operational Research - EURO XXI - OR for Better Management of Sustainable Development, .
Martens, D., Vanthienen, J., Baesens, B., & Mues, C. (2006). Measuring the consistency with prior knowledge of classification models. Paper presented at Operational Research Conference 48, .
Mues, C., Martens, D., Baesens, B., Seow, H-V., & Shahi, R. (2006). OR Techniques and Tools for Validating Basel II Models. Paper presented at 21st European Conference on Operational Research - EURO XXI - OR for Better Management of Sustainable Development, .
Setiono, R., Baesens, B., & Mues, C. (2006). Risk Management and Regulatory Compliance: a Data Mining Framework Based on Neural Network Rule Extraction. Paper presented at ICIS 2006 27th International Conference on Information Systems, .
Mues, C., Baesens, B., Setiono, R., & Vanthienen, J. (2005). From knowledge discovery to implementation: a business intelligence approach using neural network rule extraction and decision tables. In K-D. Althoff, A. Dengel, R. Bergmann, M. Nick, & T. Roth-Berghofer (Eds.), Professional Knowledge Management: Third Biennial Conference, WM 2005, Kaiserslautern, Germany, April 10-13, 2005, Revised Selected Papers (pp. 483-495). (Lecture Notes in Computer Science; No. 3782). Springer. DOI: 10.1007/11590019_55
Mues, C., Baesens, B., & Vanthienen, J. (2005). From knowledge discovery to implementation: developing business intelligence systems using decision tables. Paper presented at 3rd Conference on Professional Knowledge Management (WM2005), .
Mues, C., Baesens, B., Huysmans, J., & Vanthienen, J. (2004). Comprehensible Credit-Scoring Knowledge Visualization using Decision Tables and Diagrams. Paper presented at 6th International Conference on Enterprise Information Systems (ICEIS2004), .
Mues, C., Baesens, B., Files, C. M., & Vanthienen, J. (2004). Decision diagrams in machine learning: an empirical study on real-life credit risk data. In Diagrammatic Representation and Inference: Third International Conference, Diagrams 2004, Cambridge, UK, March 22-24, 2004. Proceedings (pp. 395-397). (Lecture Notes in Computer Science; No. 2980). Berlin / Heidelberg, Germany: Springer. DOI: 10.1007/b95854
De Troch, T., Vanthienen, J., & Mues, C. (2004). Design of a web service architecture to model business logic as a decision service. Paper presented at EUROMEDIA'2004, .
Mues, C., & Vanthienen, J. (2004). Efficient rule base verification using binary decision diagrams. Paper presented at DEXA 2004: 15h International Conference on Database and Expert Systems Applications, .
Mues, C., & Vanthienen, J. (2004). Improving the scalability of rule base verification using binary decision diagrams: an empirical study. In KI 2004: Advances in Artificial Intelligence: 27th Annual German Conference on AI, KI 2004, Ulm, Germany, September 20-24, 2004. Proceedings (pp. 381-395). (Lecture Notes in Computer Science; No. 3238). Berlin / Heidelberg, Germany: Springer. DOI: 10.1007/b100351
Mues, C., & Vanthienen, J. (2004). Improving the scalability of rule base verification using binary decision diagrams: an empirical study. Paper presented at 27th German Conference on Artificial Intelligence (KI2004), .
Huysmans, J., Baesens, B., Mues, C., & Vanthienen, J. (2004). Web Usage Mining with Time Constrained Association Rules. Paper presented at 6th International Conference on Enterprise Information Systems (ICEIS2004), .
Baesens, B., Mues, C., Setiono, R., De Backer, M., & Vanthienen, J. (2003). Building intelligent credit scoring systems using decision tables. In O. Camp, J. B. Filipe, S. Hammoudi, & M. G. Piattini (Eds.), Enterprise Information Systems V Springer.
Baesens, B., Mues, C., Setiono, R., De Backer, M., & Vanthienen, J. (2003). Building Intelligent Credit Scoring Systems using Decision Tables. Paper presented at 5th International Conference on Enterprise Information Systems (ICEIS2003), .
Baesens, B., Setiono, R., Mues, C., Viaene, S., & Vanthienen, J. (2001). Building credit-risk evaluation expert systems using neural network rule extraction and decision tables. In V. C. Storey, S. Sarkar, & J. I. DeGross (Eds.), Proceedings of the International Conference on Information Systems, ICIS 2001 (pp. 159-168). New Orleans, USA: Association for Information Systems.
Wets, G., Mues, C., & Vanthienen, J. (1999). A comparison of techniques to model decision tables from data. Paper presented at Pacific Asian Conference on Expert Systems (PACES '99), .
Vanthienen, J., Mues, C., Wets, G., & Delaere, K. (1998). A tool-supported approach to inter-tabular verification. Paper presented at 4th World Congress on Expert Systems (WCES'98), .
Martens, J., Wets, G., Vanthienen, J., & Mues, C. (1998). An initial comparison of a fuzzy neural network classifier and a decision tree based classifier. Paper presented at 4th World Congress on Expert Systems (WCES'98), .
Wets, G., Vanthienen, J., Mues, C., & Timmermans, H. (1998). Extracting complete and consistent knowledge patterns from data. Paper presented at Annual European Workshop on Validation & Verification of Knowledge-Based Systems, in conjunction with the 6th International Conference on Principles of Knowledge Representation and Reasoning (KR'98), .
Vanthienen, J., Mues, C., & Wets, G. (1997). Inter-tabular verification in an interactive environment. In J. Vanthienen, & F. van Harmelen (Eds.), Proceedings of the Fourth European Symposium on the Validation and Verification of Knowledge-Based Systems (EUROVAV'97) (pp. 155-165). Leuven, Belgium: Katholieke Universiteit.
Vanthienen, J., Aerts, A., & Mues, C. (1996). An illustration of verification and validation in the modelling phase of knowledge based systems. Paper presented at 3rd World Congress on Expert Systems (WCES '96), .
Vanthienen, J., Aerts, A., Mues, C., & Wets, G. (1995). A modelling approach to KBS verification. 155 -171. Paper presented at Proceedings of the 3rd European Symposium on the Validation and Verification of Knowledge Based Systems (EUROVAV '95), .
Vanthienen, J., Aerts, A., Mues, C., & Wets, G. (1995). A modelling approach to knowledge based systems verification. Paper presented at 11th Conference on AI for Applications (IEEE-CAIA'950) - Verification and Validation Workshop, .
Vanthienen, J., Mues, C., Aerts, A., & Wets, G. (1995). A modularization approach to the verification of knowledge based systems. Paper presented at 14th International Joint Conference on Artificial Intelligence (IJCAI'95) - Workshop on Validation & Verification of Knowledge Based Systems, .
Vanthienen, J., Mues, C., Wets, G., & Aerts, A. (1995). An integrated modelling approach to KBS development. Paper presented at Workshop on Modelling Languages for Knowledge Based Systems, Netherlands.
Vanthienen, J., Wets, G., Aerts, A., & Mues, C. (1995). Application independent tool support for the transition between knowledge structuring formalisms. Paper presented at 2nd International Symposium on Knowledge Acquisition, Representation and Processing (KARP '95), .

Working Papers

De Almeida Filho, A. T., Mues, C., & Thomas, L. C. (2009). Optimizing the collections process in consumer credit. (Discussion Papers in Centre for Operational Research, Management Science and Information Systems; No. CORMSIS-09-07). Southampton, UK: University of Southampton.
Leow, M., Mues, C., & Thomas, L. C. (2009). Predicting loss given default for residential mortgage loans: a two stage model and empirical evidence for UK bank data. (Discussion Papers in Accounting & Finance; No. CRR 09-13). England, GB: University of Southampton.
Thomas, L. C., Mues, C., & Matuszyk, A. (2007). Modelling LGD for unsecured personal loans: decision tree approach. (University of Southampton Discussion Paper Series: Centre for Operational Research, Management Sciences and Information Systems; No. CORMSIS-07-07). University of Southampton.

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

Room Number: 2/3009

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