Profile of the fraudulent customer

Anna Matuszyk; Aneta Ptak-Chmielewska

Published: 2023-11-21

Vol. 59 No. 2 (2015)

Profile of the fraudulent customer

Anna Matuszyk , Aneta Ptak-Chmielewska

Abstract

When there is an economic downturn, financial crime proliferates and people are more likely to commit fraud. One of the most common frauds is when a loan is secured without any intention of repaying it. Credit crime is a significant risk to financial institutions and has recently led to increased interest in fraud prevention systems. The most important features of such systems are the determinants (warning signals) that allow you to identify potentially fraudulent transactions. The purpose of this paper is to identify warning signals using the following data mining techniques - logistic regression, decision trees and neural networks.
Proper identification of the determinants of a fraudulent transaction can be useful in further analysis, i.e. in the segmentation process or assignment of fraud likelihood. Data obtained in this way allows profiles to be defined for fraudulent and non-fraudulent applicants. Various fraud-scoring models have been created and presented.

Keywords:

personal loan fraud , fraud determinants , profile of the fraudulent customer

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Matuszyk, A., & Ptak-Chmielewska, A. (2023). Profile of the fraudulent customer. Safe Bank, 59(2), 7–24. Retrieved from https://ojs.bfg.pl/index.php/bb/article/view/337

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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References

Books
Google Scholar

Hand, D.J. (2007): Mining personal banking data to detect fraud. In Selected Contributions in Data Analysis and Classification, ed. P. Brito, P. Bertrand, G. Cucumel, F. de Carvalho, Berlin: Springer, pp. 377–386.
Google Scholar

Journals
Google Scholar

Bolton, R.J., Hand, D.J. (2002): Statistical Fraud Detection: A Review, Statistical SciencesVol. 17, Issue 3, pp. 235–255.
Google Scholar

Delamaire, L., Abdou, H., Pointon, J., (2009): Credit card fraud and detection techniques: A review, Banks and Bank Systems, Vol. 4, Issue 2
Google Scholar

Dorfleitner, G., Jahnes, H. (2014): What factors drive personal loan fraud? Evidence from Germany, Review of Managerial Science, 1/8, pp. 89–119.
Google Scholar

Dorronsoro, J.R., Ginel, F., Sanchez, C., Cruz, C.S. (1997): Neural fraud detection in credit card operations, Neural Networks, IEEE Transactions on In Neural Networks, Vol. 8, No. 4, pp. 827–834.
Google Scholar

Hartmann-Wendels, T., Mählmann, T., Versen T. (2009): Determinants of banks’ risk exposure to new account fraud – Evidence from Germany, Journal of Banking & Finance, 01.
Google Scholar

King, G., Zeng, L. (2001): Logistic Regression in Rare Events Data, Political Analysis, No. 9, pp. 137–163.
Google Scholar

Fawcett, T., Provost, F. (1997): Adaptive Fraud Detection, Data Mining and Knowledge Discovery, Vol. 1, No. 3, pp. 291–316.
Google Scholar

Mählmann, T. (2010): On the correlation between fraud and default risk, Zeitschrift für Betriebswirtschaft, December, Volume 80, Issue 12, pp. 1325–1352.
Google Scholar

Wheeler, R., Aitken, S. (2000): Multiple algorithms for fraud detection, Knowledge-Based Systems, April, Vol. 13, No. 2–3, pp. 93–99.
Google Scholar

Whitrow, C., Hand, D.J., Juszczak, P., Weston, D., Adams, N. (2009): Transaction aggregation as a strategy for credit card fraud detection, Data Mining and Knowledge Discovery, Volume 18, Issue 1, pp. 30–55.
Google Scholar

Studies
Google Scholar

Sandrej I. (2005): Credit Fraud in retail banking, Narodna Banka Slovenska, Eurosystem, No 8.Papers from conferences and workshops
Google Scholar

Bolton, R.J., Hand, D.J. (2001): Unsupervised Profiling Methods for Fraud Detection, Credit Scoring and Credit Control VII, Edinburgh, UK, 20012.
Google Scholar

Phua, C., Lee, V., Smith, K. and Gayler, R., A comprehensive survey of data mining-based fraud detection research, The Smithsonian/NASA Astrophysics Data System, 2010, September, http://adsabs.harvard.edu/abs/2010arXiv1009.6119P, accessed 01/06/2013.
Google Scholar

Stolfo, S., Fan, W., Lee, W., Prodromidis, A., Chan, P. (1997): Credit card fraud detection using meta-learning: Issues and initial results, AAAI Workshop on AI Approaches to Fraud Detection and Risk Management.
Google Scholar

Internet web pages
Google Scholar

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/118530/annual-fraud-indicator-2012.pdf, accessed 01/06/2013.
Google Scholar

http://www.cifas.org.uk/application_fraud_novtwelve, accessed 01/06/2013.
Google Scholar