Review of Managerial Science

, Volume 8, Issue 1, pp 89–119

What factors drive personal loan fraud? Evidence from Germany

Original Paper
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Abstract

Based on a data set of nearly 43,000 personal loan applications from Germany, this paper empirically determines key factors of application fraud such as, for instance, the sales channel or the loan amount. This is done univariately as well as by employing a logistic regression, which is found to be a statistically significant approach for profiling loan application fraudsters. Besides in-sample and out-of-sample verifications, we also prove the economic significance of our results by developing a fraud management framework taking the fraud rate, the average default cost due to fraud as well as the fraud screening costs into account.

Keywords

Personal loan fraud Logistic regression Fraud management 

JEL Classification

G21 D14 K14 

References

  1. Agresti A (1990) Categorical data analysis. Wiley, New YorkGoogle Scholar
  2. Allgemeines Gleichbehandlungsgesetz (2010a) Paragraph 19a Zivilrechtliches Benachteiligungsverbot. Bundesministerium der Justiz. http://www.gesetze-im-internet.de/agg/__19.html. Accessed 15 May 2011
  3. Allgemeines Gleichbehandlungsgesetz (2010b) Paragraph 20 Zulässige unterschiedliche Behandlung. Bundesministerium der Justiz. http://www.gesetze-im-internet.de/agg/__20.html. Accessed 15 May 2011
  4. Becker GS (1968) Crime and punishment: an economic approach. J Polit Econ 76:169–217CrossRefGoogle Scholar
  5. Bolton RJ, Hand DJ (2001) Unsupervised profiling methods for fraud detection. Credit Scoring and Credit Control VII, EdinburghGoogle Scholar
  6. Bolton RJ, Hand DJ (2002) Statistical fraud detection: a review. Stat Sci 17(3):235–255CrossRefGoogle Scholar
  7. Bundesdatenschutzgesetz (2010) Paragraph 6a Automatisierte Einzelentscheidung. Bundesministerium der Justiz. http://www.gesetze-im-internet.de/bdsg_1990/__6a.html. Accessed 15 May 2010
  8. Bundeskriminalamt (2010a) Polizeiliche Kriminalstatistik, Grundtabelle—ohne Tatortverteilung—ab 1987. Wiesbaden, status, April 23rd 2010Google Scholar
  9. Bundeskriminalamt (2010b) Polizeiliche Kriminalstatistik, Aufgliederung der Tatverdächtigen—männlich—nach Alter ab 1987. Wiesbaden, status, April 23rd 2010Google Scholar
  10. Bundeskriminalamt (2010c) Polizeiliche Kriminalstatistik, Aufgliederung der Tatverdächtigen—weiblich—nach Alter ab 1987. Wiesbaden, status, April 27th 2010Google Scholar
  11. CCP Group Plc (2011) Fraud glossary of terms. http://www.cpp.co.uk/helpful-info/fraud-glossary-of-terms. Accessed 15 March 2011
  12. Costanzo CM, Halperin WC, Gale ND, Richardson GD (1982) An alternative method for assessing goodness-of-fit for logit models. Environ Plan A 14(7):963–971CrossRefGoogle Scholar
  13. Deutsche Bundesbank (2010) Zinssätze und Volumina für die Bestände und das Neugeschäft der deutschen Bank (MFIs). http://www.bundesbank.de/download/statistik/S11BATSUHDE.PDF. Accessed 18 May 2010
  14. Ehrlich I (1973) Participation in illegitimate activities: a theoretical and empirical investigation. J Polit Econ 81:521–565CrossRefGoogle Scholar
  15. Everett C (2003) UK bank fraud gang busted. Comput Fraud Secur 12:1–2Google Scholar
  16. Foster DP, Stine RA (2004) Variable selection in data mining: building a predictive model for bankruptcy. J Am Stat Assoc 99(466):303–313CrossRefGoogle Scholar
  17. Freese J, Long JS (2006) Regression models for categorical dependent variables using Stata. Stata Press, College StationGoogle Scholar
  18. Hartmann-Wendels T, Mählmann T, Versen T (2009) Determinants of banks’ risk exposure to new account fraud—evidence from Germany. J Bank Finance 33:347–357CrossRefGoogle Scholar
  19. Kreditwesengesetz (2010) Paragraph 10 Anforderungen an die Eigenmittelausstattung von Instituten, Institutsgruppen und Finanzholding-Gruppen. Bundesministerium der Justiz, http://www.gesetze-im-internet.de/kredwg/__10.html. Accessed 15 May 2010
  20. Leonard KJ (1993) The development of a rule based expert system model for fraud alert in consumer credit. Eur J Oper Res 80:350–356CrossRefGoogle Scholar
  21. Mählmann T (2010) On the correlation between fraud and default risk. Zeitschrift für Betriebswirtschaft 80:1325–1352CrossRefGoogle Scholar
  22. McCullagh P, Nelder J (1989) Generalized linear models. Chapman and Hall, LondonCrossRefGoogle Scholar
  23. Phua C, Lee V, Smith K, Gayler R (2005) A comprehensive survey of data mining-based fraud detection research. Clayton School of Information Technology, Monash University, MelbourneGoogle Scholar
  24. Porter D (2004) Identity fraud: the stealth threat to UK plc. Comput Fraud Secur 7:4–6CrossRefGoogle Scholar
  25. Schufa (2010) SCHUFA Kredit-Kompass 2010. 42, http://www.schufa-kredit-kompass.de/media/download/downloadsgesamt2010/ schufakreditkompass_2010.pdf. Accessed 25 May 2010
  26. Statistisches Bundesamt (2010) Bevölkerung und Erwerbstätigkeit—Sterbetafel Deutschland, 2007/09. http://www.destatis.de/jetspeed/portal/cms/Sites/destatis/Internet/DE/Content/Statistiken/Bevoelkerung/GeburtenSterbefaelle/Tabellen/Content100/SterbetafelDeutschland,property=file.xls. Accessed 3 April 2011
  27. Thomas, L C, Edelman, D B and Crook, J N (2002) Credit scoring and its applications. Siam—Monographs on Mathematical Modeling and Computation, PhiladelphiaGoogle Scholar
  28. Wilhelm WK (2004) The fraud management lifecycle theory: a holistic approach to fraud management. J Econ Crime Manag 2(2):1–38Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.University of RegensburgRegensburgGermany
  2. 2.BonnGermany

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