IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v61y2010i1d10.1057_jors.2009.104.html
   My bibliography  Save this article

Consumer finance: challenges for operational research

Author

Listed:
  • L C Thomas

    (School of Management, University of Southampton)

Abstract

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance.

Suggested Citation

  • L C Thomas, 2010. "Consumer finance: challenges for operational research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 41-52, January.
    • Handle: RePEc:pal:jorsoc:v:61:y:2010:i:1:d:10.1057_jors.2009.104
    DOI: 10.1057/jors.2009.104
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/jors.2009.104
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/jors.2009.104?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. H G Li & D J Hand, 2002. "Direct versus indirect credit scoring classifications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(6), pages 647-654, June.
    2. Breeden, Joseph L., 2007. "Modeling data with multiple time dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4761-4785, May.
    3. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    4. de Andrade, Fabio Wendling Muniz & Thomas, Lyn, 2007. "Structural models in consumer credit," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1569-1581, December.
    5. Yuliya Demyanyk & Otto Van Hemert, 2011. "Understanding the Subprime Mortgage Crisis," Review of Financial Studies, Society for Financial Studies, vol. 24(6), pages 1848-1880.
    6. Gestel, Tony Van & Baesens, Bart & Suykens, Johan A.K. & Van den Poel, Dirk & Baestaens, Dirk-Emma & Willekens, Marleen, 2006. "Bayesian kernel based classification for financial distress detection," European Journal of Operational Research, Elsevier, vol. 172(3), pages 979-1003, August.
    7. Thomas, Lyn C. & Edelman, David B. & Crook, Jonathan, 2004. "Readings in Credit Scoring: Foundations, Developments, and Aims," OUP Catalogue, Oxford University Press, number 9780198527978, Decembrie.
    8. Van den Poel, Dirk & Lariviere, Bart, 2004. "Customer attrition analysis for financial services using proportional hazard models," European Journal of Operational Research, Elsevier, vol. 157(1), pages 196-217, August.
    9. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405, Decembrie.
    10. Baesens, Bart & Verstraeten, Geert & Van den Poel, Dirk & Egmont-Petersen, Michael & Van Kenhove, Patrick & Vanthienen, Jan, 2004. "Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers," European Journal of Operational Research, Elsevier, vol. 156(2), pages 508-523, July.
    11. Eisenbeis, Robert A., 1978. "Problems in applying discriminant analysis in credit scoring models," Journal of Banking & Finance, Elsevier, vol. 2(3), pages 205-219, October.
    12. Bas Donkers & Peter Verhoef & Martijn Jong, 2007. "Modeling CLV: A test of competing models in the insurance industry," Quantitative Marketing and Economics (QME), Springer, vol. 5(2), pages 163-190, June.
    13. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    14. Calem, Paul S. & Gordy, Michael B. & Mester, Loretta J., 2006. "Switching costs and adverse selection in the market for credit cards: New evidence," Journal of Banking & Finance, Elsevier, vol. 30(6), pages 1653-1685, June.
    15. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    16. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
    17. Somers, Mark & Whittaker, Joe, 2007. "Quantile regression for modelling distributions of profit and loss," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1477-1487, December.
    18. Hamerle, Alfred & Liebig, Thilo & Scheule, Harald, 2004. "Forecasting Credit Portfolio Risk," Discussion Paper Series 2: Banking and Financial Studies 2004,01, Deutsche Bundesbank.
    19. Verhoef, P.C. & Donkers, A.C.D., 2001. "Predicting Customer Potential Value: an application in the insurance industry," ERIM Report Series Research in Management ERS-2001-01-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    20. D. F. Benoit & D. Van Den Poel, 2009. "Benefits of Quantile Regression for the Analysis of Customer Lifetime Value in a Contractual Setting: An Application in Financial Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/551, Ghent University, Faculty of Economics and Business Administration.
    21. Bank for International Settlements, 2005. "Stress testing at major financial institutions: survey results and practice," CGFS Papers, Bank for International Settlements, number 24, december.
    22. D J Hand & M G Kelly, 2001. "Lookahead scorecards for new fixed term credit products," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 989-996, September.
    23. J Banasik & J N Crook & L C Thomas, 1999. "Not if but when will borrowers default," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(12), pages 1185-1190, December.
    24. M Stepanova & L C Thomas, 2001. "PHAB scores: proportional hazards analysis behavioural scores," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 1007-1016, September.
    25. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    26. L C Thomas & R W Oliver & D J Hand, 2005. "A survey of the issues in consumer credit modelling research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1006-1015, September.
    27. Malhotra, Rashmi & Malhotra, D. K., 2002. "Differentiating between good credits and bad credits using neuro-fuzzy systems," European Journal of Operational Research, Elsevier, vol. 136(1), pages 190-211, January.
    28. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    29. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bellotti, Tony & Mues, Christophe, 2016. "EditorialAuthor-Name: Crook, Jonathan," European Journal of Operational Research, Elsevier, vol. 249(2), pages 395-396.
    2. Dawn Burton, 2012. "Credit Scoring, Risk, and Consumer Lendingscapes in Emerging Markets," Environment and Planning A, , vol. 44(1), pages 111-124, January.
    3. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    4. Fernando A. F. Ferreira & Ieva Meidutė-Kavaliauskienė & Edmundas K. Zavadskas & Marjan S. Jalali & Sandra M. J. Catarino, 2019. "A Judgment-Based Risk Assessment Framework for Consumer Loans," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 7-33, January.
    5. Janda, Karel & Moreira, David, 2016. "Predicting bankruptcy in European e-commerce sector," MPRA Paper 74460, University Library of Munich, Germany.
    6. J. D’Haen & D. Van Den Poel & D. Thorleuchter, 2012. "Predicting Customer Profitability During Acquisition: Finding the Optimal Combination of Data Source and Data Mining Technique," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/818, Ghent University, Faculty of Economics and Business Administration.
    7. Marchioni, Andrea & Magni, Carlo Alberto, 2018. "Investment decisions and sensitivity analysis: NPV-consistency of rates of return," European Journal of Operational Research, Elsevier, vol. 268(1), pages 361-372.
    8. Arno Botha & Conrad Beyers & Pieter de Villiers, 2019. "A procedure for loss-optimising default definitions across simulated credit risk scenarios," Papers 1907.12615, arXiv.org, revised Feb 2021.
    9. Arno Botha & Conrad Beyers & Pieter de Villiers, 2020. "Simulation-based optimisation of the timing of loan recovery across different portfolios," Papers 2009.11064, arXiv.org, revised Apr 2021.
    10. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
    11. Silva, Diego M.B. & Pereira, Gustavo H.A. & Magalhães, Tiago M., 2022. "A class of categorization methods for credit scoring models," European Journal of Operational Research, Elsevier, vol. 296(1), pages 323-331.
    12. Magni, Carlo Alberto & Marchioni, Andrea & Baschieri, Davide, 2023. "The Attribution Matrix and the joint use of Finite Change Sensitivity Index and Residual Income for value-based performance measurement," European Journal of Operational Research, Elsevier, vol. 306(2), pages 872-892.
    13. Arno Botha & Conrad Beyers & Pieter de Villiers, 2020. "The loss optimisation of loan recovery decision times using forecast cash flows," Papers 2010.05601, arXiv.org.
    14. Sumeetha Natesan & Deepika Thakur & Goutam Dutta & Manoj Kumar Tiwari, 2023. "Pricing and revenue management for bank home loans: a mathematical approach," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 656-687, June.
    15. Tang, Leilei & Thomas, Lyn & Fletcher, Mary & Pan, Jiazhu & Marshall, Andrew, 2014. "Assessing the impact of derived behavior information on customer attrition in the financial service industry," European Journal of Operational Research, Elsevier, vol. 236(2), pages 624-633.
    16. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    17. Leow, Mindy & Crook, Jonathan, 2016. "A new Mixture model for the estimation of credit card Exposure at Default," European Journal of Operational Research, Elsevier, vol. 249(2), pages 487-497.
    18. Maria Rocha Sousa & João Gama & Elísio Brandão, 2013. "Introducing time-changing economics into credit scoring," FEP Working Papers 513, Universidade do Porto, Faculdade de Economia do Porto.
    19. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn C., 2012. "Mixture cure models in credit scoring: If and when borrowers default," European Journal of Operational Research, Elsevier, vol. 218(1), pages 132-139.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
    2. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    3. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    4. G Andreeva, 2006. "European generic scoring models using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1180-1187, October.
    5. Liu, Fan & Hua, Zhongsheng & Lim, Andrew, 2015. "Identifying future defaulters: A hierarchical Bayesian method," European Journal of Operational Research, Elsevier, vol. 241(1), pages 202-211.
    6. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn C., 2012. "Mixture cure models in credit scoring: If and when borrowers default," European Journal of Operational Research, Elsevier, vol. 218(1), pages 132-139.
    7. B Baesens & T Van Gestel & M Stepanova & D Van den Poel & J Vanthienen, 2005. "Neural network survival analysis for personal loan data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1089-1098, September.
    8. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    9. Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
    10. Lessmann, Stefan & Voß, Stefan, 2009. "A reference model for customer-centric data mining with support vector machines," European Journal of Operational Research, Elsevier, vol. 199(2), pages 520-530, December.
    11. S M Finlay, 2008. "Towards profitability: a utility approach to the credit scoring problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(7), pages 921-931, July.
    12. Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
    13. repec:syb:wpbsba:03/2013 is not listed on IDEAS
    14. Jiang, Cuiqing & Wang, Zhao & Zhao, Huimin, 2019. "A prediction-driven mixture cure model and its application in credit scoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 20-31.
    15. Huei-Wen Teng & Michael Lee, 2019. "Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-27, September.
    16. Maria Rocha Sousa & João Gama & Elísio Brandão, 2013. "Introducing time-changing economics into credit scoring," FEP Working Papers 513, Universidade do Porto, Faculdade de Economia do Porto.
    17. Sanchez-Barrios, Luis Javier & Andreeva, Galina & Ansell, Jake, 2016. "“Time-to-profit scorecards for revolving credit”," European Journal of Operational Research, Elsevier, vol. 249(2), pages 397-406.
    18. Richard Chamboko & Jorge M. Bravo, 2016. "On the modelling of prognosis from delinquency to normal performance on retail consumer loans," Risk Management, Palgrave Macmillan, vol. 18(4), pages 264-287, December.
    19. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    20. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    21. Andreeva, Galina & Ansell, Jake & Crook, Jonathan, 2007. "Modelling profitability using survival combination scores," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1537-1549, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorsoc:v:61:y:2010:i:1:d:10.1057_jors.2009.104. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.