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CHAID Decision Tree: Methodological Frame and Application

References Baran, B. & Kılıç, E. (2015). Applying the CHAID algorithm to analyze how achievement is influenced by university students’ demographics, study habits, and technology familiarity. Educational Technology & Society, 18 (2), 323-335. de Ville, B. (2006). Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner. Cary, NC: SAS Institute Inc. Díaz-Pérez, M. F. & Bethencourt-Cejas, M. (2016). CHAID algorithm as an appropriate analytical method for tourism market segmentation

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Decision Tree Approach to Discovering Fraud in Leasing Agreements

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.

Open access