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Decision Tree Approach to Discovering Fraud in Leasing Agreements

Author

Listed:
  • Horvat Ivan

    (VB Leasing d.o.o., Croatia)

  • Pejić Bach Mirjana

    (Faculty of Economics & Business - Zagreb, University of Zagreb, Croatia)

  • Merkač Skok Marjana

    (Fakulteta za poslovne in komercijalne vede, Slovenia)

Abstract

Background: Fraud attempts create large losses for financing subjects in modern economies. At the same time, leasing agreements have become more and more popular as a means of financing objects such as machinery and vehicles, but are more vulnerable to fraud attempts. Objectives: The goal of the paper is to estimate the usability of the data mining approach in discovering fraud in leasing agreements. Methods/Approach: Real-world data from one Croatian leasing firm was used for creating tow models for fraud detection in leasing. The decision tree method was used for creating a classification model, and the CHAID algorithm was deployed. Results: The decision tree model has indicated that the object of the leasing agreement had the strongest impact on the probability of fraud. Conclusions: In order to enhance the probability of the developed model, it would be necessary to develop software that would enable automated, quick and transparent retrieval of data from the system, processing according to the rules and displaying the results in multiple categories.

Suggested Citation

  • Horvat Ivan & Pejić Bach Mirjana & Merkač Skok Marjana, 2014. "Decision Tree Approach to Discovering Fraud in Leasing Agreements," Business Systems Research, Sciendo, vol. 5(2), pages 61-71, September.
    • Handle: RePEc:bit:bsrysr:v:5:y:2014:i:2:p:61-71
    DOI: 10.2478/bsrj-2014-0010
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    References listed on IDEAS

    as
    1. Coussement, Kristof & Van den Bossche, Filip A.M. & De Bock, Koen W., 2014. "Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees," Journal of Business Research, Elsevier, vol. 67(1), pages 2751-2758.
    2. McCarty, John A. & Hastak, Manoj, 2007. "Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression," Journal of Business Research, Elsevier, vol. 60(6), pages 656-662, June.
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    Cited by:

    1. Milanović Marina & Stamenković Milan, 2016. "CHAID Decision Tree: Methodological Frame and Application," Economic Themes, Sciendo, vol. 54(4), pages 563-586, December.

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    More about this item

    Keywords

    decision tree; fraud detection; leasing fraud; cars; data mining; leasing agreements;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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