Pregled bibliografske jedinice broj: 717285
Decision Tree Approach to Discovering Fraud in Leasing Agreements
Decision Tree Approach to Discovering Fraud in Leasing Agreements // Business systems research, 5 (2014), 2; 61-71 doi:10.2478/bsrj-2014-0010 (podatak o recenziji nije dostupan, članak, znanstveni)
CROSBI ID: 717285 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Decision Tree Approach to Discovering Fraud in Leasing Agreements
Autori
Horvat, Ivan ; Pejić Bach, Mirjana ; Merkač Skok, Marjana
Izvornik
Business systems research (1847-8344) 5
(2014), 2;
61-71
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
decision tree; fraud detection; leasing fraud; cars; data mining; leasing agreements
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Ekonomija, Informacijske i komunikacijske znanosti
Citiraj ovu publikaciju:
Časopis indeksira:
- Web of Science Core Collection (WoSCC)
- Emerging Sources Citation Index (ESCI)
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- INSPEC