Abstract
Conventional techniques for detecting outliers address the problem of finding isolated observations that significantly differ from other observations that are stored in a database. For example, in the context of health insurance, one might be interested in finding unusual claims concerning prescribed medicines. Each claim record may contain information on the prescribed drug (its code), volume (e.g., the number of pills and their weight), dosing and the price. Finding outliers in such data can be used for identifying fraud. However, when searching for fraud, it is more important to analyse data not on the level of single records, but on the level of single patients, pharmacies or GP’s.
In this paper we present a novel approach for finding outliers in such hierarchical data. Our method uses standard techniques for measuring outlierness of single records and then aggregates these measurements to detect outliers in entities that are higher in the hierarchy. We applied this method to a set of about 40 million records from a health insurance company to identify suspicious pharmacies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agyemang, A., Barker: A comprehensive survey of numeric and symbolic outlier mining techniques. Intelligent Data Analysis 10(6/2006), 521–538 (2005)
Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 43–78. Springer, Heidelberg (2002)
Bain, Engelhardt: Introduction to Probability and Mathematical Statistics. Duxbury Press, Boston (1992)
Barnett, V., Lewis, T.: Outliers in Statistical Data. John Wiley and Sons, Chichester (1994)
Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. SIGMOD Rec. 29(2), 93–104 (2000)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Computing Surveys (CSUR) 41, 15:1–15:58 (2009)
Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)
Knorr, E.M., Ng, R.T.: Algorithms for mining distance-based outliers in large datasets. In: Unknown, pp. 392–403 (1998)
Kriegel, H.-P., Kröger, P., Schubert, E., Zimek, A.: Loop: local outlier probabilities. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 1649–1652. ACM, New York (2009)
Papadimitriou, S., Kitagawa, H., Gibbons, P.B., Faloutsos, C.: Loci: Fast outlier detection using the local correlation integral. In: International Conference on Data Engineering, p. 315 (2003)
Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. SIGMOD Rec. 29, 427–438 (2000)
Rousseeuw, P.J., Driessen, K.V.: A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212–223 (1999)
Tang, J., Chen, Z., Chee Fu, A.W., Cheung, D.: A robust outlier detection scheme for large data sets. In: 6th Pacific-Asia Conf. on Knowledge Discovery and Data Mining, pp. 6–8 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Konijn, R.M., Kowalczyk, W. (2011). Finding Fraud in Health Insurance Data with Two-Layer Outlier Detection Approach. In: Cuzzocrea, A., Dayal, U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2011. Lecture Notes in Computer Science, vol 6862. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23544-3_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-23544-3_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23543-6
Online ISBN: 978-3-642-23544-3
eBook Packages: Computer ScienceComputer Science (R0)