Abstract
Record linkage is a pivotal data integration stage in the vehicle insurance claims analysis system and serves as a foundation for fraud detection, market promotion and other major business applications. While the traditional method of rules based classification plus clerical review is still in use in the industry, the latest development has advanced into link analysis based collective record linkage which has put the blocking and classification processes under the global context. To apply this method with a fraud detection objective, we have developed a community enhanced record linkage model specially tailored for the requirements of vehicle insurance claim system. A major novel approach is the construction of claim communities linking the claims, customers and vehicles involved and apply probabilistic data matching algorithms integrated with spatio-temporal co-occurrence patterns. In addition, the matched results could be used to identify the outliers in fraud detection analysis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Combating insurance claims fraud. Technical report, SAS (2012)
Barber, D.: Bayesian Reasoning and Machine Learning. Cambridge University Press, Cambridge (2010)
Bolton, R.J., Hand, D.J.: Statistical fraud detection: a review. Stat. Sci. 17, 235–255 (2002)
Boongoen, T.: Discovering identity in intelligence data using weighted link based similarities: a case study of Thailand (2015)
Brockmann, D., Hufnagel, L., Geisel, T.: The scaling laws of human travel. Nature 439(7075), 462–465 (2006). https://doi.org/10.1038/nature04292. https://www.ncbi.nlm.nih.gov/pubmed/16437114
Christen, P.: Data Matching Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, Berlin (2012)
David, J., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., Kleinberg, J.: Inferring social ties from geographic coincidences. Proc. Nat. Acad. Sci. 107, 22436–22441 (2010)
Fellegi, I.P., Sunter, A.B.: A theory for record linkage. J. Am. Stat. Assoc. 64, 1183–1210 (1969)
Government, Q.: Travel in South-East Queensland an analysis of travel data from 1992 to 2009. Technical report (2012)
Newcombe, H.B., Kennedy, J.M., Axford, S.J., James, A.P.: Automatic linkage of vital records. Science 130, 954–959 (1959)
Bhattacharya, I., Getoor, L.: Collective entity resolution in relational data. ACM Trans. Knowl. Disc. Data 1, 5 (2007)
Nin, J., Munt’es-Mulero, V., Martinez-Bazan, N., Larriba-Pey, J.L.: On the use of semantic blocking techniques for data cleansing and integration. In: 11th International Database Engineering and Applications Symposium (IDEAS 2007) (2007)
Liu, J., et al.: Graph analysis for detecting fraud, waste and abuse in healthcare data. In: Association for the Advancement of Artificial Intelligence (www.aaai.org) (2015)
Kalashinikov, D.V., Mehrotra, S.: Domain-independent data cleaning via analysis of entity-relationship graph. ACM Trans. Database Syst. 31, 716–767 (2006)
Kouki, P., Pujara, J., Marcum, C., Koehly, L., Getoor, L.: Collective entity resolution in familial networks. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 227–236 (2017). https://doi.org/10.1109/icdm.2017.32
Wang, P., Hunter, T., Bayen, A.M., Schechtner, K., González, M.C.: Understanding road usage patterns in urban areas. Sci. Rep. (2012)
DuVall, S.L., Kerber, R.A., Thomas, A.: Extending the Fellegi-Sunter probabilistic record linkage method for approximate field comparators. J. Biomed. Inf. 43, 24–30 (2010)
Sun, L., Axhausen, K.W., Lee, D.H., Huang, X.: Understanding metropolitan patterns of daily encounters. Proc. Nat. Acad. Sci. U.S.A. 110(34), 13774–9 (2013). https://doi.org/10.1073/pnas.1306440110. https://www.ncbi.nlm.nih.gov/pubmed/23918373
Herzog, T.N., Scheuren, F.J., Winkler, W.E.: Data Quality And Record Linkage Techniques. Springer, Berlin (2007)
Viaene, S., Dedene, G.: Insurance fraud-issues and challenges. In: 2004 The International Association for the Study of Insurance Economics (2004)
Rastogi, V., Dalvi, N., Garofalakis, M.: Large-scale collective entity matching. Proc. VLDB Endowment 4, 208–218 (2009)
Cohen, W.W., Ravikumar, P., Fienberg, S.E.: A comparison of string distance metrics for name-matching tasks (2003)
Christen, P., Churches, T., Willmore, A.: A probabilistic geocoding system based on a national address file (2004)
Dong, X., Halevy, A.: Reference reconciliation in complex information spaces (2005)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lu, C., Huang, G., Xiang, Y. (2019). Community Enhanced Record Linkage Method for Vehicle Insurance System. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-35231-8_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35230-1
Online ISBN: 978-3-030-35231-8
eBook Packages: Computer ScienceComputer Science (R0)