Credit Risk

Description

Given the on-going turmoil on credit markets, a critical re-assessment of current capital and credit risk modelling approaches is more than ever needed. Especially the interplay between credit risk and other types of risk (e.g. market risk, liquidity risk, interest rate risk, concentration risk, etc.) has been poorly addressed.

Professor Baesens’ research group endeavours to come up with new and ground-breaking approaches for better credit risk modelling and is currently focussing on five research tracks:

Developing optimal credit risk model calibration procedures (PD / LGD / EAD)

Credit risk models aim to discriminate obligors and/or exposures in terms of default (PD), loss (LGD) and exposure (EAD) risk. However, losses need to be absorbed by capital in an absolute way! Hence, we aim to develop calibration procedures that come up with optimal cardinal measures of risk taking into account both past experience and future expectations. Therefore we need to study which statistical techniques work well on a time series of historical credit risk data and how survival analysis can be used to work with different time horizons (see Survival Analysis). Furthermore, we will investigate the impact of both point-in-time (PIT) and through-the-cycle (TTC) calibration.

Devising new frameworks for monitoring and validating credit risk parameters (PD / LGD / EAD)

Ground-breaking approaches for monitoring and validating the economic capital model parameters have to be devised. Theoretical contributions will necessitate new approaches for model backtesting, model benchmarking and qualitative validation of the various economic capital parameters. Besides, we must find out how to correctly measure data quality and we must set up data quality programs (see Data Quality). In addition, it is necessary to investigate how to quantify model performance, model comprehensibility and model readability at the model level.

Introducing innovative economic capital stress testing procedures

New approaches for stress testing the economic capital model are necessary. Using stress testing, economic capital is affected by extreme, adverse economic circumstances, similar to the recent events in the credit markets. The research will focus on defining stress scenarios, integrating various types of risk, understanding the behaviour in the tail of the loss distribution, and gauging the impact on credit risk and capital positions at the firm level. It will be required to define solid, financial theories in case of limited historical data.

Developing new ways to quantify and deal with model risk

Risk quantification models are not perfect and as such also have risk associated with them. It is important to be aware of this and to appropriately act upon it, using e.g. conservative parameter calibration and/or other novel approaches. Therefore, we endeavour to define model risk based on data quality and model limitations and to relate economic downturn calibration to stress testing and study the implications.

Credit risk models for microfinance

See: Microfinance.

Notable publications

Journal Publications

  • CASTERMANS G., MARTENS D., VAN GESTEL T., HAMERS B., BAESENS B., An Overview and Framework for PD Backtesting and Benchmarking, Journal of the Operational Research Society, vol. 61, number 3, pp. 359-373, 2010
  • VAN LAERE E., BAESENS B., The development of a simple and intuitive rating system under Solvency II,Insurance: Mathematics and Economics, Forthcoming 2010
  • MARTENS D., VAN GESTEL T., DE BACKER M., HAESEN R., VANTHIENEN J., BAESENS B., Credit Rating Prediction Using Ant Colony Optimization, Journal of the Operational Research Society, vol. 61, pp. 561-573, 2010
  • SETIONO R. , BAESENS B., MUES C., A note on knowledge discovery using neural networks and its application to credit card screening, European Journal of Operational Research, vol. 192, number 1, pp. 326-332, 2009
  • VAN LAERE E., BAESENS B., THIBEAULT A., Bank capital: a myth resolved, Tijdschrift voor Bank en Financiewezen, vol. 1, 2008
  • VAN GESTEL T., MARTENS D., BAESENS B., FEREMANS D., HUYSMANS J., VANTHIENEN J., Forecasting and Analyzing Insurance Companies’ Ratings, International Journal of Forecasting, vol. 23, number 3, pp. 513-529, 2007
  • MARTENS D., BAESENS B., VAN GESTEL T., VANTHIENEN J., Comprehensible Credit Scoring Models Using Rule Extraction From Support Vector Machines, European Journal of Operational Research, vol. 183, pp. 1466-1476, 2007
  • HOFFMANN F., BAESENS B., MUES C., VAN GESTEL T., VANTHIENEN J., Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms, European Journal of Operational Research, vol. 177, number 1, pp. 540-555, 2006
  • VAN GESTEL T., BAESENS B., VAN DIJCKE P., SUYKENS J., GARCIA J., ALDERWEIRELD T., Linear and nonlinear credit scoring by combining logistic regression and support vector machines, Journal of Credit Risk, vol. 1, number 4, 2005
  • VAN GESTEL T., BAESENS B., VAN DIJCKE P., GARCIA J., SUYKENS J.A.K., VANTHIENEN J., A process model to develop an internal rating system: sovereign credit ratings, Decision Support Systems, vol. 42, number 2, pp. 1131-1151, 2006
  • HUYSMANS J., BAESENS B., VAN GESTEL T., VANTHIENEN J., Failure prediction with self organizing maps ,Expert Systems With Applications, vol. 30, number 3, pp. 479-487, 2006
  • SOMOL P., BAESENS B., PUDIL P., VANTHIENEN J., Filter-versus Wrapper-based Feature Selection for Credit Scoring, International Journal of Intelligent Systems, vol. 20, number 10, pp. 985-999, 2005
  • BAESENS B., VAN GESTEL T., MUES C., VANTHIENEN J., Intelligent Information Systems for Financial Engineering, Expert Systems With Applications, vol. 30, number 3, pp. 413-414, 2006
  • VAN GESTEL T., BAESENS B., SUYKENS J.A.K., VAN DEN POEL D., BAESTAENS D.-E., WILLEKENS M., Kernel Based Classification for Financial Distress Detection, European Journal of Operational Research, vol. 172, number 3, pp. 979-1003, 2006
  • BAESENS B., VAN GESTEL T., STEPANOVA M., VAN DEN POEL D., VANTHIENEN J., Neural Network Survival Analysis for Personal Loan Data, Journal of the Operational Research Society, vol. 59, number 9, pp. 1089-1098, 2005
  • MUES C., BAESENS B., FILES C.M., VANTHIENEN J., Decision Diagrams in Machine Learning: an Empirical Study on Real-Life Credit-Risk Data, Expert Systems with Applications, vol. 27, number 2, pp. 257-264, 2004
  • BAESENS B., SETIONO R., MUES C., VANTHIENEN J., Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation, Management Science, vol. 49, number 3, pp. 312-329, 2003
  • VAN GESTEL T., BAESENS B., GARCIA J., VAN DIJCKE P., A Support Vector Machine Approach to Credit Scoring, Bank en Financiewezen, vol. 2, pp. 73-82, 2003
  • HOFFMANN F., BAESENS B., MARTENS J., PUT F., VANTHIENEN J., Comparing a Genetic Fuzzy and a Neurofuzzy Classifier for Credit Scoring, International Journal of Intelligent Systems, vol. 17, number 11, pp. 1067-1083, 2002
  • LIMA E., MUES C., BAESENS B., Monitoring and Backtesting Churn Models, Expert Systems with Applications, Forthcoming 2010
  • VAN GESTEL T., MARTENS D., BAESENS B., From Linear to Non-linear Kernel Based Classifiers for Bankruptcy Prediction, Neurocomputing, Forthcoming 2010

Book Publications

  • BAESENS B., Developing Intelligent Systems for Credit Scoring Using Machine Learning Techniques, Ph.D. Thesis, K.U.Leuven, 2003
  • VAN GESTEL T., BAESENS B., Credit Risk Management: Model Risk Control, Oxford University Press, Forthcoming 2010
  • VAN GESTEL T., BAESENS B., Credit Risk Management: Quantitative Modeling, Oxford University Press, Forthcoming 2010

Dutch Journal Publications

  • DEJAEGER K., RUELENS J., VAN GESTEL T., JACKOBS J., BAESENS B., Evaluatie en verbetering van de datakwaliteit, Informatie, vol. 51, number 9, 2009

Conference Publications

  • VAN LAERE E., BAESENS B., The Development of a Simple and Intuitive Rating System under Solvency II,Proceedings of the Midwest Finance Association Conference, Las Vegas, NV, U.S.A., February 2010
  • LOTERMAN G., BROWN I., MARTENS D., MUES C., BAESENS B., Benchmarking state-of-the-art regression algorithms for loss given default modelling, Proceedings of the Conference on Credit Scoring and Credit Control, Edingburgh, United Kingdom, August 2009
  • MARTENS D., VAN GESTEL T., VANDEN BRANDE K., JACOBS J., BAESENS B., A Practical Framework for Credit Risk Stress Testing, Proceedings of the Conference on Credit Scoring and Credit Control, Edinburgh, United Kingdom, August 2009
  • VAN LAERE E., BAESENS B., Regulatory and economic capital: theory and practice, evidence from the field,Proceedings of the International Risk Management Conference 2009, Financial instability. A new world framework?, Venice, Italy, June 2009
  • VAN LAERE E., BAESENS B., The development of a simple and intuitive rating system under Solvency II,Proceedings of the International Risk Management Conference (IRMC 2008), Credit and Financial Risk Management: 40 years after the Altman Z-score model, Florence, Italy, June 2008
  • SETIONO R., BAESENS B., MUES C., Risk Management and Regulatory Compliance: A Data Mining Framework Based on Neural Network Rule Extraction, Proceedings of the International Conference on Information Systems (ICIS 2006), , Milwaukee, Wisconsin, U.S.A., pp. 71-85, December 2006  Best Paper Design Track
  • VAN GESTEL T., SUYKENS J., PELCKMANS K., BAESENS B., Credit Rating Systems by Combining Linear Ordinal Logistic Regression and Fixed-Size Least Squares Support Vector Machines, NIPS 2005 Conference, Workshop on Machine Learning in Finance, Whistler, Britisch Columbia, Canada, December 2005
  • HUYSMANS J., BAESENS B., VANTHIENEN J., A comprehensible SOM-based Scoring System, Proceedings of the International Conference on Machine Learning and Data Mining (MLDM 2005) , Leipzig, Germany, pp. 80-89, July 2005
  • MUES C., BAESENS B., HUYSMANS J., VANTHIENEN J., Comprehensible Credit-Scoring Knowledge Visualization Using Decision Tables And Diagrams, Proceedings of the Sixth International Conference on Enterprise Information Systems (ICEIS 2004), Porto, Portugal, pp. 226-232, April 2004
  • MUES C., BAESENS B., FILES C., VANTHIENEN J., Decision diagrams in machine learning: an empirical study on real-life credit-risk data, Proceedings of the Third International Conference on the Theory and Application of Diagrams (Diagrams 2004), Cambridge, United Kingdom, March 2004
  • BAESENS B., VAN GESTEL T., STEPANOVA M., VANTHIENEN J., Neural Network Survival Analysis for Personal Loan Data, Proceedings of the Eighth Conference on Credit Scoring and Credit Control (CSCCVII 2003) , Edinburgh, United Kingdom, September 2003
  • EGMONT-PETERSEN M., BAESENS B., FEELDERS A., Using Bayesian Networks for Estimating the Risk of Default in Credit Scoring, Proceedings of the International Workshop on Computational Management Science, Economics, Finance and Engineering, Limassol, Cyprus, March 2003
  • BAESENS B., MUES C., SETIONO R., DE BACKER M., VANTHIENEN J., Building Intelligent Credit Scoring Systems using Decision Tables, Proceedings of the Fifth International Conference on Enterprise Information Systems (ICEIS 2003), Angers, France, pp. 19-25, April 2003  Best Paper Nomination
  • VAN GESTEL T., BAESENS B., SUYKENS J., ESPINOZA M., BAESTAENS D.E., VANTHIENEN J., DE MOOR B., Bankruptcy Prediction with Least Squares Support Vector Machine Classifiers, Proceedings of the IEEE International Conference on Computational Intelligence for Financial Engineering (CIFEr 2003), Hong Kong, , March 2003
  • BUCKINX W., BAESENS B., VAN DEN POEL D., VAN KENHOVE P., VANTHIENEN J., Using Machine Learning Techniques to Predict Defection of Top Clients, Proceedings of the Third International Conference on Data Mining Methods and Databases for Engineering, Finance and Other Fields, Bologna, Italy, pp. 509-517, September 2002
  • BAESENS B., EGMONT-PETERSEN M., CASTELO R., VANTHIENEN J., Learning Bayesian Network Classifiers for Credit Scoring using Markov Chain Monte Carlo Search, Proceedings of the Sixteenth International Conference on Pattern Recognition (ICPR 2002), Quebec, Canada, pp. 49-52, August 2002
  • BAESENS B., SETIONO R., MUES C., VIAENE S., VANTHIENEN J., Building Credit-Risk Evaluation Expert Systems using Neural Network Rule Extraction and Decision Tables, Proceedings of the Twenty Second International Conference on Information Systems (ICIS 2001), New Orleans, Louisiana, U.S.A., December 2001
  • BAESENS B., SETIONO R., DE LILLE V., VIAENE S., VANTHIENEN J., Neural Network Rule Extraction for Credit Scoring, Proceedings of The Pacific Asian Conference on Intelligent Systems (PAIS 2001) , Seoul, South Korea, pp. 128-132, November 2001
  • BAESENS B., VIAENE S., VANTHIENEN J., A Comparative Study of State of the Art Classification Algorithms for Credit Scoring, Proceedings of the Seventh Conference on Credit Scoring and Credit Control (CSCCVII 2001), Edinburgh, United Kingdom, September 2001
  • VAN GOOL J., BAESENS B., SERCU P., VERBEKE W., An Analysis of the Applicability of Credit Scoring for Microfinance, The Academic and Business Research Institute Conference, Orlando, U.S.A., September 2009
  • VERBEKE W., DEJAEGER K., MARTENS D., BAESENS B., Customer churn prediction: does technique matter ?,Joint Statistical Meeting, Vancouver , Canada, July 2010