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A discrete choice approach to model credit card fraud

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  • Pulina, Manuela
  • Paba, Antonello

Abstract

This paper analyses the demographic, socio-economics and banking specific determinants that influence the risk of fraud in a portfolio of credit cards. The data are from recent account archives for cards issued throughout Italy. A logit framework is employed that incorporates cards at a risk of fraud as the dependent variable and a set of explanatory variables (e.g. gender, location, credit line, number of transactions in euros and in non euros currency). The empirical results provide useful indicators on the factors that are responsible for potential risk of fraud.

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File URL: http://mpra.ub.uni-muenchen.de/20019/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 20019.

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Date of creation: Apr 2010
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Handle: RePEc:pra:mprapa:20019

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Keywords: credit card; fraud; demographic and socio-economics factors; logit modelling.;

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  1. Greene, William, 1998. "Sample selection in credit-scoring models1," Japan and the World Economy, Elsevier, vol. 10(3), pages 299-316, July.
  2. Peter Burns & Anne Stanley, 2002. "Fraud management in the credit card industry," Payment Cards Center Discussion Paper 02-05, Federal Reserve Bank of Philadelphia.
  3. 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.
  4. Dionne, G., 1980. "The Effects of Insurance on the Possibilities of Fraud," Cahiers de recherche 8103, Universite de Montreal, Departement de sciences economiques.
  5. Feldman, Roger, 2001. "An Economic Explanation for Fraud and Abuse in Public Medical Care Programs," The Journal of Legal Studies, University of Chicago Press, vol. 30(2), pages 569-77, June.
  6. Steven B. Caudill & Mercedes Ayuso & Montserrat Guillén, 2005. "Fraud Detection Using a Multinomial Logit Model With Missing Information," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 72(4), pages 539-550.
  7. Claire E. Crutchley & Marlin R. H. Jensen & Beverly B. Marshall, 2007. "Climate for Scandal: Corporate Environments that Contribute to Accounting Fraud," The Financial Review, Eastern Finance Association, vol. 42(1), pages 53-73, 02.
  8. Oya Pinar Ardic & Uygar Yuzereroglu, 2006. "A Multinomial Logit Model of Bank Choice: An Application to Turkey," Working Papers 2006/02, Bogazici University, Department of Economics.
  9. Crook, Jonathan & Banasik, John, 2004. "Does reject inference really improve the performance of application scoring models?," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 857-874, April.
  10. M. Martin Boyer, 2007. "Resistance (to Fraud) Is Futile," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(2), pages 461-492.
  11. Artis, Manuel & Ayuso, Mercedes & Guillen, Montserrat, 1999. "Modelling different types of automobile insurance fraud behaviour in the Spanish market," Insurance: Mathematics and Economics, Elsevier, vol. 24(1-2), pages 67-81, March.
  12. 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.
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