Abstract
A deep understanding of different aspects of business performance and operations is necessary for a leading insurance company to maintain its position on the market and make further development. This chapter presents a clustering analysis for target group identification by locality, based on a case study in the motor insurance industry. Soft computing techniques have been applied to understand the business and customer patterns by clustering data sets sourced from policy transactions and policyholders’ profiles. Self organizing map clustering and k-means clustering are used to perform the segmentation tasks in this study. Such clustering analysis can also be employed as a predictive tool for other applications in the insurance industry, which are discussed in this chapter.
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Wang, X., Keogh, E. (2008). A Clustering Analysis for Target Group Identification by Locality in Motor Insurance Industry. In: Prasad, B. (eds) Soft Computing Applications in Business. Studies in Fuzziness and Soft Computing, vol 230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79005-1_7
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DOI: https://doi.org/10.1007/978-3-540-79005-1_7
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