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A Fuzzy Random Survival Forest for Predicting Lapses in Insurance Portfolios Containing Imprecise Data

Author

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  • Jorge Luis Andrade

    (Faculty of Statistics, Complutense University, Puerta de Hierro, 28040 Madrid, Spain)

  • José Luis Valencia

    (Faculty of Statistics, Complutense University, Puerta de Hierro, 28040 Madrid, Spain)

Abstract

We propose a fuzzy random survival forest (FRSF) to model lapse rates in a life insurance portfolio containing imprecise or incomplete data such as missing, outlier, or noisy values. Following the random forest methodology, the FRSF is proposed as a new machine learning technique for solving time-to-event data using an ensemble of multiple fuzzy survival trees. In the learning process, the combination of methods such as the c-index, fuzzy sets theory, and the ensemble of multiple trees enable the automatic handling of imprecise data. We analyse the results of several experiments and test them statistically; they show the FRSF’s robustness, verifying that its generalisation capacity is not reduced when modelling imprecise data. Furthermore, the results obtained using a real portfolio of a life insurance company demonstrate that the FRSF has a better performance in comparison with other state-of-the-art algorithms such as the traditional Cox model and other tree-based machine learning techniques such as the random survival forest.

Suggested Citation

  • Jorge Luis Andrade & José Luis Valencia, 2022. "A Fuzzy Random Survival Forest for Predicting Lapses in Insurance Portfolios Containing Imprecise Data," Mathematics, MDPI, vol. 11(1), pages 1-16, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:198-:d:1020068
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    References listed on IDEAS

    as
    1. Marco Aleandri & Alessia Eletti, 2021. "Modelling dynamic lapse with survival analysis and machine learning in CPI," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 37-56, June.
    2. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
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    Cited by:

    1. Daniel Doz & Darjo Felda & Mara Cotič, 2023. "Demographic Factors Affecting Fuzzy Grading: A Hierarchical Linear Regression Analysis," Mathematics, MDPI, vol. 11(6), pages 1-19, March.

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