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

Xin Che ()
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Xin Che: California State University

The Geneva Papers on Risk and Insurance - Issues and Practice, 2025, vol. 50, issue 1, No 8, 203-231

Abstract: 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.

Keywords: Big data analytics; Loss reserve accuracy; Property-liability insurance (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1057/s41288-024-00336-x

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