IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v18y2011i2-3p59-88.html
   My bibliography  Save this article

Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature

Author

Listed:
  • Hussein A. Abdou
  • John Pointon

Abstract

Credit scoring has been regarded as a core appraisal tool of different institutions during the last few decades and has been widely investigated in different areas, such as finance and accounting. Different scoring techniques are being used in areas of classification and prediction, where statistical techniques have conventionally been used. Both sophisticated and traditional techniques, as well as performance evaluation criteria, are investigated in the literature. The principal aim of this paper, in general, is to carry out a comprehensive review of 214 articles/books/theses that involve credit scoring applications in various areas but in particular primarily in finance and banking. This paper also aims to investigate how credit scoring has developed in importance and to identify the key determinants in the construction of a scoring model, by means of a widespread review of different statistical techniques and performance evaluation criteria. Our review of literature revealed that there is no overall best statistical technique used in building scoring models and the best technique for all circumstances does not yet exist. Also, the applications of the scoring methodologies have been widely extended to include different areas, and this subsequently can help decision makers, particularly in banking, to predict their clients' behaviour. Finally, this paper also suggests a number of directions for future research. Copyright © 2011 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
  • Handle: RePEc:wly:isacfm:v:18:y:2011:i:2-3:p:59-88
    DOI: 10.1002/isaf.325
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/isaf.325
    Download Restriction: no

    File URL: https://libkey.io/10.1002/isaf.325?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
    ---><---

    References listed on IDEAS

    as
    1. J Banasik & J Crook & L Thomas, 2001. "Scoring by usage," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 997-1006, September.
    2. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    3. Hussein A. Abdou, 2009. "An evaluation of alternative scoring models in private banking," Journal of Risk Finance, Emerald Group Publishing, vol. 10(1), pages 38-53, January.
    4. DeYoung, Robert & Frame, W. Scott & Glennon, Dennis & McMillen, Daniel P. & Nigro, Peter, 2008. "Commercial lending distance and historically underserved areas," Journal of Economics and Business, Elsevier, vol. 60(1-2), pages 149-164.
    5. Desai, Vijay S. & Crook, Jonathan N. & Overstreet, George A., 1996. "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, Elsevier, vol. 95(1), pages 24-37, November.
    6. 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.
    7. Foglia, A. & Laviola, S. & Marullo Reedtz, P., 1998. "Multiple banking relationships and the fragility of corporate borrowers," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1441-1456, October.
    8. Eric Rosenberg & Alan Gleit, 1994. "Quantitative Methods in Credit Management: A Survey," Operations Research, INFORMS, vol. 42(4), pages 589-613, August.
    9. Jen Shang & Yu-sen Lin & Angella Goetz, 2000. "Diagnosis of MRSA with neural networks and logistic regression approach," Health Care Management Science, Springer, vol. 3(4), pages 287-297, September.
    10. Lensberg, Terje & Eilifsen, Aasmund & McKee, Thomas E., 2006. "Bankruptcy theory development and classification via genetic programming," European Journal of Operational Research, Elsevier, vol. 169(2), pages 677-697, March.
    11. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    12. Falbo, P, 1991. "Credit-scoring by enlarged discriminant models," Omega, Elsevier, vol. 19(4), pages 275-289.
    13. Yang, Yingxu, 2007. "Adaptive credit scoring with kernel learning methods," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1521-1536, December.
    14. Emel, Ahmet Burak & Oral, Muhittin & Reisman, Arnold & Yolalan, Reha, 2003. "A credit scoring approach for the commercial banking sector," Socio-Economic Planning Sciences, Elsevier, vol. 37(2), pages 103-123, June.
    15. 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.
    16. Hoffmann, F. & Baesens, B. & Mues, C. & Van Gestel, T. & Vanthienen, J., 2007. "Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 177(1), pages 540-555, February.
    17. Hussein A. Abdou & John Pointon, 2009. "Credit scoring and decision making in Egyptian public sector banks," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 5(4), pages 391-406, September.
    18. Jerzy Stefanowski & Szymon Wilk, 2001. "Evaluating business credit risk by means of approach‐integrating decision rules and case‐based learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 97-114, June.
    19. G Verstraeten & D Van den Poel, 2005. "The impact of sample bias on consumer credit scoring performance and profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 981-992, August.
    20. Heuson, Andrea & Passmore, Wayne & Sparks, Roger, 2001. "Credit Scoring and Mortgage Securitization: Implications for Mortgage Rates and Credit Availability," The Journal of Real Estate Finance and Economics, Springer, vol. 23(3), pages 337-363, November.
    21. Altman, Edward I., 2005. "An emerging market credit scoring system for corporate bonds," Emerging Markets Review, Elsevier, vol. 6(4), pages 311-323, December.
    22. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    23. David Durand, 1941. "Risk Elements in Consumer Instalment Financing," NBER Books, National Bureau of Economic Research, Inc, number dura41-1, March.
    24. Haughwout, Andrew & Peach, Richard & Tracy, Joseph, 2008. "Juvenile delinquent mortgages: Bad credit or bad economy?," Journal of Urban Economics, Elsevier, vol. 64(2), pages 246-257, September.
    25. Malhotra, Rashmi & Malhotra, D. K., 2003. "Evaluating consumer loans using neural networks," Omega, Elsevier, vol. 31(2), pages 83-96, April.
    26. 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.
    27. Dasgupta, Chanda Ghose & Dispensa, Gary S. & Ghose, Sanjoy, 1994. "Comparing the predictive performance of a neural network model with some traditional market response models," International Journal of Forecasting, Elsevier, vol. 10(2), pages 235-244, September.
    28. 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.
    29. McKee, Thomas E. & Lensberg, Terje, 2002. "Genetic programming and rough sets: A hybrid approach to bankruptcy classification," European Journal of Operational Research, Elsevier, vol. 138(2), pages 436-451, April.
    30. Steenackers, A. & Goovaerts, M. J., 1989. "A credit scoring model for personal loans," Insurance: Mathematics and Economics, Elsevier, vol. 8(1), pages 31-34, March.
    31. Chen, Yenpao & Guo, Ruey-Ji & Huang, Rao-Li, 2009. "Two stages credit evaluation in bank loan appraisal," Economic Modelling, Elsevier, vol. 26(1), pages 63-70, January.
    32. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
    33. 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.
    34. 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.
    35. Banasik, John & Crook, Jonathan, 2007. "Reject inference, augmentation, and sample selection," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1582-1594, December.
    36. W. Scott Frame & Lynn Woosley, 2004. "Credit Scoring and the Availability of Small Business Credit in Low‐ and Moderate‐Income Areas," The Financial Review, Eastern Finance Association, vol. 39(1), pages 35-54, February.
    37. Liang, Qi, 2003. "Corporate Financial Distress Diagnosis in China : Empirical Analysis Using Credit Scoring Models," Hitotsubashi Journal of commerce and management, Hitotsubashi University, vol. 38(1), pages 13-28, October.
    38. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    39. Orgler, Yair E, 1970. "A Credit Scoring Model for Commercial Loans," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 2(4), pages 435-445, November.
    40. J Banasik & J Crook & L Thomas, 2003. "Sample selection bias in credit scoring models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 822-832, August.
    41. 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.
    42. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    43. Hu, Yu-Chiang & Ansell, Jake, 2007. "Measuring retail company performance using credit scoring techniques," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1595-1606, December.
    44. Greene, William, 1998. "Sample selection in credit-scoring models1," Japan and the World Economy, Elsevier, vol. 10(3), pages 299-316, July.
    45. Eisenbeis, Robert A, 1977. "Pitfalls in the Application of Discriminant Analysis in Business, Finance, and Economics," Journal of Finance, American Finance Association, vol. 32(3), pages 875-900, June.
    46. Carter, David A. & McNulty, James E., 2005. "Deregulation, technological change, and the business-lending performance of large and small banks," Journal of Banking & Finance, Elsevier, vol. 29(5), pages 1113-1130, May.
    47. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring," European Journal of Operational Research, Elsevier, vol. 195(3), pages 942-959, June.
    48. J Banasik & J Crook, 2005. "Credit scoring, augmentation and lean models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1072-1081, September.
    49. J. S. Cramer, 2004. "Scoring bank loans that may go wrong: a case study," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(3), pages 365-380, August.
    50. J J Glen, 2001. "Classification accuracy in discriminant analysis: a mixed integer programming approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(3), pages 328-339, March.
    51. Paleologo, Giuseppe & Elisseeff, André & Antonini, Gianluca, 2010. "Subagging for credit scoring models," European Journal of Operational Research, Elsevier, vol. 201(2), pages 490-499, March.
    52. 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.
    53. Boyes, William J. & Hoffman, Dennis L. & Low, Stuart A., 1989. "An econometric analysis of the bank credit scoring problem," Journal of Econometrics, Elsevier, vol. 40(1), pages 3-14, January.
    54. J Banasik & J Crook, 2010. "Reject inference in survival analysis by augmentation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 473-485, March.
    55. Nikolopoulos, K. & Goodwin, P. & Patelis, A. & Assimakopoulos, V., 2007. "Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches," European Journal of Operational Research, Elsevier, vol. 180(1), pages 354-368, July.
    56. Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
    57. Dinh, Thi Huyen Thanh & Kleimeier, Stefanie, 2007. "A credit scoring model for Vietnam's retail banking market," International Review of Financial Analysis, Elsevier, vol. 16(5), pages 471-495.
    58. Seow, Hsin-Vonn & Thomas, Lyn C., 2006. "Using adaptive learning in credit scoring to estimate take-up probability distribution," European Journal of Operational Research, Elsevier, vol. 173(3), pages 880-892, September.
    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. Rais Ahmad Itoo & A. Selvarasu & José António Filipe, 2015. "Loan Products and Credit Scoring by Commercial Banks (India)," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(1), pages 851-851.
    2. Ha Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," Working Papers hal-04133309, HAL.
    3. Nicolas Suhadolnik & Jo Ueyama & Sergio Da Silva, 2023. "Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach," JRFM, MDPI, vol. 16(12), pages 1-21, November.
    4. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    5. Kritzinger, Nico & van Vuuren, Gary Wayne, 2021. "Non-capital calibration of bureau scorecards," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 260-271.

    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. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    2. Rais Ahmad Itoo & A. Selvarasu & José António Filipe, 2015. "Loan Products and Credit Scoring by Commercial Banks (India)," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(1), pages 851-851.
    3. 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.
    4. Ha Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," Working Papers hal-04133309, HAL.
    5. 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.
    6. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    7. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    8. Rogelio A. Mancisidor & Michael Kampffmeyer & Kjersti Aas & Robert Jenssen, 2019. "Deep Generative Models for Reject Inference in Credit Scoring," Papers 1904.11376, arXiv.org, revised Sep 2021.
    9. José Willer Prado & Valderí Castro Alcântara & Francisval Melo Carvalho & Kelly Carvalho Vieira & Luiz Kennedy Cruz Machado & Dany Flávio Tonelli, 2016. "Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1007-1029, March.
    10. Şaban Çelik & Bora Aktan & Bruce Burton, 2022. "Firm dynamics and bankruptcy processes: A new theoretical model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 567-591, April.
    11. 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.
    12. Runchi Zhang & Zhiyi Qiu, 2020. "Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-35, June.
    13. 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.
    14. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    15. 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.
    16. 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.
    17. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    18. Ha-Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," EconomiX Working Papers 2016-10, University of Paris Nanterre, EconomiX.
    19. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    20. Ha Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," Working Papers hal-04141601, HAL.

    More about this item

    Statistics

    Access and download statistics

    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:wly:isacfm:v:18:y:2011:i:2-3:p:59-88. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    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.