Leveraging Text Mining for Enhanced Decision-Making in the Insurance Industry

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Abstract

This research article delves into the transformative role of text mining within the insurance industry, particularly focusing on the property and casualty (P&C) sector. The ability to extract actionable insights from vast amounts of unstructured data-such as claim narratives, adjuster notes, and customer communications-has become crucial for insurers in today’s competitive landscape. This paper explores how text mining can be effectively utilized in underwriting, claims processing, and fraud detection, thereby enhancing decision-making and improving business outcomes. Additionally, it discusses a structured, process-driven approach to implementing text mining using pensource technologies in insurance companies. The research also addresses the challenges and future prospects of text mining in this sector.

References

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Yeo D, Kramer K, Park S, et al. (2017) Text mining for the analysis of insurance claims: A comparative study of computational techniques. The Journal of Supercomputing, 73(1): 58-76.

[bib1] Aggarwal CC, Zhai C (2012) Mining text data. Springer Science & Business Media.

[bib2] Balakrishnan K (2010) Leveraging text mining in insurance. ISO Innovative Analytics.

[bib3] Chen H, Chiang RHL, Storey VC (2012) Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4): 1165-1188.

[bib4] Davenport TH, Dyché J (2013) Big data in big companies. International Institute for Analytics.

[bib5] Feldman R, Sanger J (2007) The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press.

[bib6] Miner G, Elder IV J, Fast A, et al. (2012) Practical text mining and statistical analysis for non-structured text data applications. Academic Press.

[bib7] Viaene S, Baesens B, Van Gestel T, et al. (2005) Knowledge discovery in a direct marketing case using least squares support vector machines. International Journal of Intelligent Systems, 20(4): 415-428.

[bib8] Weiss SM, Indurkhya N, Zhang T, et al. (2005) Text mining: Predictive methods for analyzing unstructured information. Springer Science & Business Media.

[bib9] Wüthrich MV, Merz M (2010) Statistical modeling for claims reserves: with applications to run-off triangles. Springer Science & Business Media.

[bib10] Yeo D, Kramer K, Park S, et al. (2017) Text mining for the analysis of insurance claims: A comparative study of computational techniques. The Journal of Supercomputing, 73(1): 58-76.