<abstract xmlns="http://www.w3.org/1999/xhtml"><p><bold>Background:</bold> 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. <bold>Objectives:</bold> The goal of the paper is to estimate the usability of the data mining approach in discovering fraud in leasing agreements. <bold>Methods/Approach:</bold> 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. <bold>Results:</bold> The decision tree model has indicated that the object of the leasing agreement had the strongest impact on the probability of fraud. <bold>Conclusions:</bold> 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.</p></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.

Decision Tree Approach to Discovering Fraud in Leasing Agreements

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

Fakulteta za poslovne in komercijalne vede, Slovenia

Business Systems Research Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Decision Tree Approach to Discovering Fraud in Leasing Agreements"}

Background: Fraud attempts create large losses for financing subjects in modern economies. At the same time, leasing agreements have become more and more...

Decision Tree Approach to Discovering Fraud in Leasing Agreements

Online veröffentlicht: 10. Sept. 2014

Seitenbereich: 61 - 71

Eingereicht: 21. Sept. 2013

Akzeptiert: 28. März 2014

DOI: https://doi.org/10.2478/bsrj-2014-0010

Schlüsselwörter
decision tree, fraud detection, leasing fraud, cars, data mining, leasing agreements

© 2014 Ivan Horvat et. al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Decision Tree Approach to Discovering Fraud in Leasing Agreements

Online veröffentlicht: 10. Sept. 2014

Seitenbereich: 61 - 71

Eingereicht: 21. Sept. 2013

Akzeptiert: 28. März 2014

DOI: https://doi.org/10.2478/bsrj-2014-0010

Schlüsselwörterdecision tree, fraud detection, leasing fraud, cars, data mining, leasing agreements

© 2014 Ivan Horvat et. al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
decision tree, fraud detection, leasing fraud, cars, data mining, leasing agreements