IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v28y2012i1p216-223.html
   My bibliography  Save this article

Overcoming selectivity bias in evaluating new fraud detection systems for revolving credit operations

Author

Listed:
  • Hand, David J.
  • Crowder, Martin J.

Abstract

When proposed new fraud detection systems are tested in revolving credit operations, a straightforward comparison of the observed fraud detection rates is subject to a selectivity bias that tends to favour the existing system. This bias arises from the fact that accounts are terminated when the existing system, but not the proposed new system, detects a fraudulent transaction. This therefore flatters the estimated detection rate of the existing system. We develop more formal estimators that can be used to compare the existing and proposed new systems without risking this effect. We also assess the magnitude of the bias.

Suggested Citation

  • Hand, David J. & Crowder, Martin J., 2012. "Overcoming selectivity bias in evaluating new fraud detection systems for revolving credit operations," International Journal of Forecasting, Elsevier, vol. 28(1), pages 216-223.
    • Handle: RePEc:eee:intfor:v:28:y:2012:i:1:p:216-223
    DOI: 10.1016/j.ijforecast.2010.10.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207011000124
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2010.10.005?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. 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.
    2. 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.
    3. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    4. Juszczak, Piotr & Adams, Niall M. & Hand, David J. & Whitrow, Christopher & Weston, David J., 2008. "Off-the-peg and bespoke classifiers for fraud detection," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4521-4532, May.
    5. D J Hand & C Whitrow & N M Adams & P Juszczak & D Weston, 2008. "Performance criteria for plastic card fraud detection tools," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(7), pages 956-962, July.
    Full references (including those not matched with items on IDEAS)

    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. Y Kim & S Y Sohn, 2007. "Technology scoring model considering rejected applicants and effect of reject inference," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(10), pages 1341-1347, October.
    2. 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.
    3. Ha-Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," EconomiX Working Papers 2016-10, University of Paris Nanterre, EconomiX.
    4. Ha Thu Nguyen, 2016. "Reject inference in application scorecards: evidence from France," Working Papers hal-04141601, HAL.
    5. 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.
    6. 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.
    7. 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.
    8. Thi Mai Luong, 2020. "Selection Effects of Lender and Borrower Choices on Risk Measurement, Management and Prudential Regulation," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2020, January-A.
    9. Darima Fotheringham & Michael A. Wiles, 2023. "The effect of implementing chatbot customer service on stock returns: an event study analysis," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 802-822, July.
    10. Song, Wei-Ling & Uzmanoglu, Cihan, 2016. "TARP announcement, bank health, and borrowers’ credit risk," Journal of Financial Stability, Elsevier, vol. 22(C), pages 22-32.
    11. Raymundo M. Campos-Vázquez, 2013. "Efectos de los ingresos no reportados en el nivel y tendencia de la pobreza laboral en México," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 23-54, November.
    12. Jonathan Gruber & Aaron Yelowitz, 1999. "Public Health Insurance and Private Savings," Journal of Political Economy, University of Chicago Press, vol. 107(6), pages 1249-1274, December.
    13. Campbell, Randall C. & Nagel, Gregory L., 2016. "Private information and limitations of Heckman's estimator in banking and corporate finance research," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 186-195.
    14. Leye Li & Louise Yi Lu & Dongyue Wang, 2022. "External labour market competitions and stock price crash risk: evidence from exposures to competitor CEOs’ award‐winning events," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(S1), pages 1421-1460, April.
    15. Calcagno, R. & Renneboog, L.D.R., 2004. "Capital Structure and Managerial Compensation : The Effects of Renumeration Seniority," Discussion Paper 2004-120, Tilburg University, Center for Economic Research.
    16. Son K. Lam & Thomas E. DeCarlo & Ashish Sharma, 2019. "Salesperson ambidexterity in customer engagement: do customer base characteristics matter?," Journal of the Academy of Marketing Science, Springer, vol. 47(4), pages 659-680, July.
    17. McCausland, David & Pouliakas, Konstantinos & Theodossiou, Ioannis, 2005. "Some are Punished and Some are Rewarded: A Study of the Impact of Performance Pay on Job Satisfaction," MPRA Paper 14243, University Library of Munich, Germany.
    18. Gary F. Peters & Andrea M. Romi & Juan Manuel Sanchez, 2019. "The Influence of Corporate Sustainability Officers on Performance," Journal of Business Ethics, Springer, vol. 159(4), pages 1065-1087, November.
    19. Fossen, Frank M. & König, Johannes, 2015. "Public health insurance and entry into self-employment," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112934, Verein für Socialpolitik / German Economic Association.
    20. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).

    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:eee:intfor:v:28:y:2012:i:1:p:216-223. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.