Abstract
The Social Insurance industry can be considered as a basic pillar of the welfare state in many countries around the world. However, there is not much public research work on how to prevent social fraud. And the few published works are oriented towards detecting fraud on the side of the employees or providers. In this work, our aim is to describe our experience when designing and implementing a data-driven approach for fraud detection but in relation to employers not meeting their obligations. In fact, we present here a case study in Upper Austria but from which interesting lessons can be drawn to be applied in a wide range of different situations.
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Notes
- 1.
Oberösterreichische Gebietskrankenkasse (short OÖGKK).
References
Diaz-Granados, M., Diaz Montes, J., Parashar, M.: Investigating insurance fraud using social media. In: BigData 2015, pp. 1344–1349 (2015)
Dua, P., Bais, S.: Supervised learning methods for fraud detection in healthcare insurance. In: Dua, S., Acharya, U.R., Dua, P. (eds.) Machine Learning in Healthcare Informatics. ISRL, vol. 56, pp. 261–285. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-40017-9_12
Hofmarcher, M.M., Quentin, W.: Austria: health system review. Health Syst. Trans. 15(7), 1–292 (2013)
Konijn, R.M., Kowalczyk, W.: Finding fraud in health insurance data with two-layer outlier detection approach. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 394–405. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23544-3_30
Kose, I., Gokturk, M., Kilic, K.: An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance. Appl. Soft Comput. 36, 283–299 (2015)
Lu, F., Boritz, J.E.: Detecting fraud in health insurance data: learning to model incomplete Benford’s law distributions. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 633–640. Springer, Heidelberg (2005). https://doi.org/10.1007/11564096_63
Rao, B.: The role of medical data analytics in reducing health fraud and improving clinical and financial outcomes. In: CBMS 2013, p. 3 (2013)
Rawte, V., Anuradha, G.: Fraud detection in health insurance using data mining techniques. In: 2015 International Conference on Communication, Information and Computing Technology (ICCICT), Mumbai, pp. 1–5 (2015)
Shi, Y., Tian, Y., Kou, G., Peng, Y., Li, J.: Health insurance fraud detection. In: Shi, Y., Tian, Y., Kou, G., Peng, Y., Li, J. (eds.) Optimization Based Data Mining: Theory and Applications. AI&KP. Springer, London (2011). https://doi.org/10.1007/978-0-85729-504-0_14
Sun, C., Li, Q., Li, H., Shi, Y., Zhang, S., Guo, W.: Patient cluster divergence based healthcare insurance fraudster detection. IEEE Access 7, 14162–14170 (2019)
Thornton, D., van Capelleveen, G., Poel, M., van Hillegersberg, J., Müller, R.M.: Outlier-based health insurance fraud detection for U.S. medicaid data. In: ICEIS, no. 2, pp. 684–694 (2014)
Tsai, Y., Ko, C., Lin, K.: Using CommonKADS method to build prototype system in medical insurance fraud detection. JNW 9(7), 1798–1802 (2014)
Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., Baesens, B.: GOTCHA! network-based fraud detection for social security fraud. Manag. Sci. 63(9), 3090–3110 (2017)
Widder, A., von Ammon, R., Hagemann, G., Schoenfeld, D.: An approach for automatic fraud detection in the insurance domain. In: AAAI Spring Symposium Intelligent Event Processing, pp. 98–100 (2009)
Yang, W.-S., Hwang, S.-Y.: A process-mining framework for the detection of healthcare fraud and abuse. Expert Syst. Appl. 31(1), 56–68 (2006)
Acknowledgments
The research reported in this paper has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the Province of Upper Austria in the frame of the COMET center SCCH.
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Himmelbauer, J., Martinez-Gil, J., Ksen, M., Linner, K., Plakolm, S. (2019). Towards a Data-Driven Approach for Fraud Detection in the Social Insurance Field: A Case Study in Upper Austria. In: Kő, A., Francesconi, E., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Electronic Government and the Information Systems Perspective. EGOVIS 2019. Lecture Notes in Computer Science(), vol 11709. Springer, Cham. https://doi.org/10.1007/978-3-030-27523-5_6
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