Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services

Benedek Botond () and László Ede
Additional contact information
Benedek Botond: Babeş-Bolyai University, Romania
László Ede: Babeş-Bolyai University, Romania

Studia Universitatis Babeș-Bolyai Oeconomica, 2019, vol. 64, issue 2, 53-71

Abstract: Customer segmentation represents a true challenge in the automobile insurance industry, as datasets are large, multidimensional, unbalanced and it also requires a unique price determination based on the risk profile of the customer. Furthermore, the price determination of an insurance policy or the validity of the compensation claim, in most cases must be an instant decision. Therefore, the purpose of this research is to identify an easily usable data mining tool that is capable to identify key automobile insurance fraud indicators, facilitating the segmentation. In addition, the methods used by the tool, should be based primarily on numerical and categorical variables, as there is no well-functioning text mining tool for Central Eastern European languages. Hence, we decided on the SQL Server Analysis Services (SSAS) tool and to compare the performance of the decision tree, neural network and Naïve Bayes methods. The results suggest that decision tree and neural network are more suitable than Naïve Bayes, however the best conclusion can be drawn if we use the decision tree and neural network together.

Keywords: automobile insurance; insurance fraud; fraud indicators; data mining (search for similar items in EconPapers)
JEL-codes: C49 C88 G22 K42 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/subboec-2019-0009 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:subboe:v:64:y:2019:i:2:p:53-71:n:4

DOI: 10.2478/subboec-2019-0009

Access Statistics for this article

Studia Universitatis Babeș-Bolyai Oeconomica is currently edited by Dumitru Matis

More articles in Studia Universitatis Babeș-Bolyai Oeconomica from Sciendo
Bibliographic data for series maintained by Peter Golla ().