Abstract
The aim of this paper is detection of organized groups of fraudsters causing more leakage revenue for insurance industry, automobile in particular. Networks play significant roles in representing any relations between entities; thus, they are effective in distinguishing organized fraud activities. We proposed a method by which the groups of perpetrators whose relation form are cycles in the network of accidents.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
White Paper. (2011). The insurance fraud race: Using information and analytics to stay ahead of criminals. Featuring as an example: SAS® Fraud Framework for Insurance.
Nian, K., Zhang, H., Tayal, A., Coleman, T., & Li, Y. (2016). Auto insurance fraud detection using unsupervised spectral ranking for anomaly. The Journal of Finance and Data Science, 2(1), 58–75.
Brockett, P. L., & Levine, A. (1977). Characterization of RIDITs. Annals of Statistics, 5(6), 1245–1248.
Šubelj, L., Furlan, Š., & Bajec, M. (2011). An expert system for detecting automobile insurance fraud using social network analysis. Expert Systems with Applications, 38(1), 1039–1052. https://doi.org/10.1016/j.eswa.2010.07.143.
Zhou, J., Xu, W., Guo, X., & Ding, J. (2015). A method for modeling and analysis of directed weighted accident causation network (DWACN). Physica A: Statistical Mechanics and Its Applications, 437, 263–277.
Noble, C. C., & Cook, D. J. (2003). Graph-based anomaly detection. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and datamining (pp. 631–636).
Holder, L. B., & Cook, D. J. (2003). Graph-based relational learning: Current and future directions. SIGKDD Explorations, 5(1), 90–93.
Maxion, R. A., & Tan, K. M. C. (2000). Benchmarking anomaly-based detection systems. In Proceedings of the international conference on dependable systems and networks (pp. 623–630).
Sun, J., Qu, H., Chakravarti, D., & Faloutsos, C. (2005). Relevance search and anomaly detection in bipartite graphs. ACM SIGKDD Explorations Newsletter, 7(2), 48–55.
Freeman, L. C. (1979). Centrality in social networks—Conceptual clarification. Social Networks, 1(3), 215–239.
Cormen, T., Leiserson, C., Rivest, R., & Stein, C. (2002). Introduction to algorithms (2nd ed.p. 1312). Cambridge, MA: The MIT Press ISBN: 9780262033848.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Bodaghi, A., Teimourpour, B. (2018). Automobile Insurance Fraud Detection Using Social Network Analysis. In: Moshirpour, M., Far, B., Alhajj, R. (eds) Applications of Data Management and Analysis . Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-95810-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-95810-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95809-5
Online ISBN: 978-3-319-95810-1
eBook Packages: Business and ManagementBusiness and Management (R0)