Decision Tree Approach to Discovering Fraud in Leasing Agreements

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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.

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  • 1. Apté C. Weiss S. (1997) “Data mining with decision trees and decision rules” Future Generation Computer Systems Vol. 13 No. 2-3 pp. 197-210.

  • 2. Bhattacharyya S. et al. (2011) “Data mining for credit card fraud: A comparative study” Decision Support Systems Vol. 50 No. 3 pp. 602-613.

  • 3. Coussement K. Van den Bossche F. A. De Bock K. W. (2014) “Data accuracy’s impact on segmentation performance: Benchmarking RFM analysis logistic regression and decision trees” Journal of Business Research Vol. 67 No. 1 pp. 2751-2758.

  • 4. Huang S. Y. Tsaih R. H. Lin W. Y. (2012) “Unsupervised neural networks approach for understanding fraudulent financial reporting” Industrial Management & Data Systems Vol. 112 No. 2 pp. 224-244.

  • 5. Li X. B. (2005) “A scalable decision tree system and its application in pattern recognition and intrusion detection” Decision Support Systems Vol. 41 No. 1 pp.112-130.

  • 6. McCarty J. A. Hastak M. (2007) “Segmentation approaches in data-mining: A comparison of RFM CHAID and logistic regression” Journal of Business Research Vol. 60 No. 6 pp. 656-662.

  • 7. Morais A. I. (2013) “Why companies choose to lease instead of buy? Insights from academic literature” Academia Revista Latinoamericana de Administración Vol. 26 No. 3 pp. 432-446.

  • 8. Ngai E.W.T. et al. (2011) “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature” Decision Support Systems Vol. 50 No. 3 pp. 559-569.

  • 9. Sinha A.T. Zhao H. (2008) “Incorporating domain knowledge into data mining classifiers: An application in indirect lending” Decision Support Systems Vol. 46 No. 1 pp. 287-299.

  • 10. Smith C. W. Wakeman L. M. (1985) “Determinants of corporate leasing activity” Journal of Finance Vol. 40 No. 3 pp. 895-911.

  • 11. Tsang S. et al. (2011) “Decision trees for uncertain data” Knowledge and Data Engineering IEEE Transactions on Vol. 23 No. 1 pp. 64-78.

  • 12. Wu S. X. Banzhaf W. (2010) “The use of computational intelligence in intrusion detection systems: A review” Applied Soft Computing Vol. 10 No. 1 pp. 1-35.

Journal information
Impact Factor

CiteScore 2018: 0.57

SCImago Journal Rank (SJR) 2018: 0.165
Source Normalized Impact per Paper (SNIP) 2018: 0.388

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