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Investment in big data analytics and loss reserve accuracy: evidence from the U.S. property-liability insurance industry

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  • Xin Che

    (California State University)

Abstract

This study explores the impact of big data analytics investment on loss reserve accuracy in the U.S. property-liability insurance industry. Utilising a dataset of 1243 insurers from 2002 to 2016, we find a significant association between higher investment in big data analytics and more accurate loss reserve estimates. Our analysis distinguishes between over-reserving and under-reserving behaviours, revealing that big data analytics contributes to the reduction of both. The study employs entropy balancing, internal instrumental variable estimation and errors-in-variables regressions to enhance the robustness of the findings. This research not only fills a gap in the academic literature but also provides practical implications for enhancing the precision of loss reserve estimates through technological investments.

Suggested Citation

  • Xin Che, 2025. "Investment in big data analytics and loss reserve accuracy: evidence from the U.S. property-liability insurance industry," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 50(1), pages 203-231, January.
    • Handle: RePEc:pal:gpprii:v:50:y:2025:i:1:d:10.1057_s41288-024-00336-x
    DOI: 10.1057/s41288-024-00336-x
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