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Predictive Modelling of Claim Frequency Social Protection (SOCSO’S Survivors’ Pension Benefits)

Author

Listed:
  • M. Z. A. Chek

    (Actuarial Science Department, UiTM Perak Branch)

  • I. L. Ismail

    (Department of Statistics and Decision Science, UiTM Perak Branch)

  • M. S. Asrulsani

    (Actuarial Science Department, UiTM Perak Branch)

  • H. Hasim

    (School of Mathematical & Computer Sciences Heriot-Watt University, UK)

  • Z. H. Zulkifli

    (Actuarial Partners Consulting, Malaysia)

Abstract

This study employs predictive analytics to forecast the frequency of claims for SOCSO’s Survivors’ Pension Benefits from 2025 to 2030. Using historical data from 1985 to 2024, we develop predictive models employing ARIMA, decision trees, and artificial neural networks (ANN). The study aims to provide a comprehensive understanding of claim frequency trends and their potential impact on SOCSO’s financial sustainability. The ARIMA model captures time-series patterns, decision trees identify key determinants influencing claim fluctuations, and ANN detects complex, non-linear relationships within the dataset. The study’s findings indicate a continuous upward trajectory in claim numbers, with projections revealing an annual growth rate of approximately 5.5%. These results highlight the necessity for SOCSO to adjust its financial strategies, optimize actuarial assumptions, and implement data-driven policy interventions to ensure the long-term sustainability of the fund. By integrating predictive analytics, SOCSO can proactively manage risk, optimize contribution rates, and maintain financial stability while continuing to provide adequate support for beneficiaries. The insights generated from this study will be instrumental for policymakers in making informed decisions regarding future pension fund allocations and social security planning.

Suggested Citation

  • M. Z. A. Chek & I. L. Ismail & M. S. Asrulsani & H. Hasim & Z. H. Zulkifli, 2025. "Predictive Modelling of Claim Frequency Social Protection (SOCSO’S Survivors’ Pension Benefits)," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(3), pages 856-862, March.
    • Handle: RePEc:bcp:journl:v:9:y:2025:issue-3:p:856-862
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    References listed on IDEAS

    as
    1. Haberman, Steven & Renshaw, Arthur, 2012. "Parametric mortality improvement rate modelling and projecting," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 309-333.
    2. Mohd Zaki Awang Chek & Isma Liana Ismail, 2024. "Understanding Strategic Enhancements for the Coverage, Efficiency, and Sustainability of the Social Security System in Malaysia," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(4), pages 1723-1730, April.
    3. M.Z.A. Chek & I.L. Ismail, 2021. "Issues and Challenges Social Insurance in Malaysia," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(4), pages 278-281, April.
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