Abstract
The paper presents the findings of an industry-based study in the utility of text mining. The purpose of the study was to evaluate the impact of textual information in claims cost prediction. The industrial research setting was a large Australian insurance company. The data mining methodologies used in this research included text mining, and the application of the results from the text mining in subsequent predictive data mining models. The researchers used software of the leading commercial vendors. The research found commercially interesting utility in textual information for claim cost prediction, and also identified new risk management factors.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Berry, M.J.A., Linoff, G.S.: Data Mining Techniques for Marketing, Sales, and Customer Relationship Management. Wiley, Indianapolis (2004)
Dedobbeleer, N., Béland, F.: Is risk perception one of the dimensions of safety climate? In: Feyer, A.-M., Williamson, A. (eds.) Occupational Injury: Risk, Prevention and Intervention, pp. 73–81. Taylor & Francis, London (1998)
Ellingworth, M., Sullivan, D.: Text Mining Improves Business Intelligence and Predictive Modelling in Insurance (2005), http://www.dwreview.com/article_subcfm?articleId=6995.html (accessed February 2005)
Feldman, R.: Mining Text Data. In: Ye, N. (ed.) The Handbook of Data Mining, pp. 481–517. Lawrence Erlbaum Associates, London (2003)
Feyer, A.-M., Stout, N., et al.: Use of narrative analysis for comparisons of the causes of fatal accidents in three countries: New Zealand, Australia, and the United States. Injury Prevention 7, i15–i20 (2001)
Feyer, A.-M., Williamson, A.: Introduction. In: Feyer, A.-M., Williamson, A. (eds.) Occupational Injury: Risk, Prevention and Intervention, pp. 1–3. Taylor & Francis, London (1998)
Hastie, T., Tibshirani, R., et al.: The elements of statistical learning: Data mining, inference and prediction. Springer, New York (2001)
Kolyshkina, I., Steinberg, D., et al.: Using Data Mining for Modeling Insurance Risk and Comparison of Data Mining and Linear Modeling Approaches. In: Shapiro, A.F., Jain, L.C. (eds.) Intelligent and Other Computational Techniques in Insurance: Theory and Applications, vol. 6, pp. 421–493. World Scientific, London (2003)
Mailvaganam, H.: Text Mining for Fraud Detection: Creating cost effective data mining solutions for fraud analysis (2005), http://www.dwreview.com/Data_mining/Effective_Text_Mining.html (accessed February 2005)
Phua, C., Lee, V., et al.: A Comprehensive Survey of Data Mining-based Fraud Detection Research (submitted)
Stout, N.: Analysis of narrative text fields in occupational injury data. In: Feyer, A.-M., Williamson, A. (eds.) Occupational Injury: Risk, Prevention and Intervention, pp. 15–20. Taylor & Francis, London (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kolyshkina, I., van Rooyen, M. (2006). Text Mining for Insurance Claim Cost Prediction. In: Williams, G.J., Simoff, S.J. (eds) Data Mining. Lecture Notes in Computer Science(), vol 3755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677437_15
Download citation
DOI: https://doi.org/10.1007/11677437_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32547-5
Online ISBN: 978-3-540-32548-2
eBook Packages: Computer ScienceComputer Science (R0)