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Readings in Credit Scoring: Foundations, Developments, and Aims

Author

Listed:
  • Thomas, Lyn C.

    (School of Management, University of Southampton)

  • Edelman, David B.

    (Direct Line Financial Services)

  • Crook, Jonathan

    (Professor in Business Economics School of Management, University of Edinburgh)

Abstract

Credit scoring is one of the most successful applications of statistical and management science techniques in finance in the last forty years. This unique collection of recent papers, with comments by experts in the field, provides excellent coverage of recent developments, advances and aims in credit scoring. Aimed at statisticians, economists, operational researchers and mathematicians working in both industry and academia, and to all working on credit scoring and data mining, it is an invaluable source of reference. Contributors to this volume - R W Johnson R Eisenbeis M A Hopper and E M Lewis A D Wilkie G Wilkinson and J Tingay R L Keeney and R M Oliver A Lucas R M Oliver and E Wells D J Hand and W E Henley G A Overstreet Jr., E L Bradley, and R S Kemp Jr. J N Crook, L C Thomas, and R Hamilton G Bennett, G Platts, and J Crossley K J Leonard G Platts and I Howe A Lucas and J Powell B Narain P Sewart and J Whittaker M B Yobas, J N Crook, and P Ross J Ho, L C Thomas, T A Pomrey, and W T Scherer

Suggested Citation

  • Thomas, Lyn C. & Edelman, David B. & Crook, Jonathan, 2004. "Readings in Credit Scoring: Foundations, Developments, and Aims," OUP Catalogue, Oxford University Press, number 9780198527978.
    • Handle: RePEc:oxp:obooks:9780198527978
    as

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    Citations

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    Cited by:

    1. Jonathan K. Budd & Peter G. Taylor, 2015. "Calculating optimal limits for transacting credit card customers," Papers 1506.05376, arXiv.org, revised Aug 2015.
    2. José Carlos Trejo-García & Miguel Ángel Martínez-García & Francisco Venegas-Martínez, 2017. "Administración del riesgo crediticio al menudeo en México: una mejora econométrica en la selección de variables y cambios en sus características," Contaduría y Administración, Accounting and Management, vol. 62(2), pages 11-12, Abril-Jun.
    3. S M Finlay, 2006. "Predictive models of expenditure and over-indebtedness for assessing the affordability of new consumer credit applications," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(6), pages 655-669, June.
    4. K Bijak, 2011. "Kalman filtering as a performance monitoring technique for a propensity scorecard," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 29-37, January.
    5. José Carlos Trejo-García & Miguel Ángel Martínez-García & Francisco Venegas-Martínez, 2017. "Credit risk management at retail in Mexico: An econometric improvement in the selection of variables and changes in their characteristics," Contaduría y Administración, Accounting and Management, vol. 62(2), pages 13-14, Abril-Jun.
    6. Rais Ahmad Itoo & A. Selvarasu, 2017. "Loan products and Credit Scoring Methods by Commercial Banks," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 7(1), pages 1297-1297.
    7. Ivan Tikshaev & Roman Kulshin & Gennadii Volokitin & Pavel Senchenko & Anatoly Sidorov, 2022. "The Possibilities of Using Scoring to Determine the Relevance of Software Development Tenders," Mathematics, MDPI, vol. 10(24), pages 1-13, December.
    8. J Whittaker & C Whitehead & M Somers, 2007. "A dynamic scorecard for monitoring baseline performance with application to tracking a mortgage portfolio," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(7), pages 911-921, July.
    9. 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.
    10. Izabela Majer, 2006. "Application scoring: logit model approach and the divergence method compared," Working Papers 17, Department of Applied Econometrics, Warsaw School of Economics.

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