Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

DOI: http://dx.doi.org/10.15439/2025F2264

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 759–764 (2025)

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Abstract. Classical life tables assume fully observed lifespans and ignore censoring, which can bias survival estimates. In this study, we compare the classical life table with two censoring-aware approaches: the Kaplan-Meier estimator applied to simulated censored lifetimes, and the actuarial estimator assuming uniform censoring within intervals. Using mortality data for the Czech Republic (from year 2021), we show that both the mentioned alternative methods yield systematically lower survival and life expectancy estimates, compared to life tables-based approach. The actuarial estimator is the most conservative, and we provide a formal proof that it underestimates survival relative to Kaplan-Meier. These differences have practical implications for actuarial pricing and longevity risk. We advocate incorporating survival analysis techniques into actuarial workflows when dealing with incomplete or censored data.

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