Actuarial Vulnerabilities in Social Protection Systems of Emerging Economies: A Dual Systematic Review of Pension Sustainability and Commuting Hazard Liabilities

Authors

M. Z. A. Chek

Actuarial Science Department, UiTM Perak Branch (Malaysia)

I. L. Ismail

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

E. N. I. Hashim

Actuarial Science Department, UiTM N. Sembilan Branch (Malaysia)

Z. H. Zulkifli

Actuarial Partners Consulting (Malaysia)

Rinda Nariswari

Department of Computer Science, BINUS Indonesia (Malaysia)

Article Information

DOI: 10.47772/IJRISS.2026.10200273

Subject Category: Social science

Volume/Issue: 10/2 | Page No: 3767-3773

Publication Timeline

Submitted: 2026-02-22

Accepted: 2026-02-27

Published: 2026-03-06

Abstract

Social protection systems in emerging and middle-income economies are confronting structural actuarial vulnerabilities driven by rapid demographic transition, labour market informality, and evolving occupational risk patterns. This paper develops an integrated research framework to examine two interrelated and financial material liabilities within statutory social insurance schemes: (i) the long-term sustainability and adequacy of survivors’ and invalidity pension programs under accelerated population ageing and shrinking contributor bases, and (ii) the rising actuarial burden of commuting-related occupational accidents characterised by high tail-risk exposure.

Keywords

Social Security, Survivors' Pension, Commuting Accidents

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References

1. Department of Statistics Malaysia (DOSM), “Demographic Statistics and Aging Population Projections,” 2025. [Google Scholar] [Crossref]

2. C. Freudenberg and others, “How to design survivor benefits in the 21st century,” 2022. [Google Scholar] [Crossref]

3. M. Z. Awang Chek, “Optimizing contribution rate of Socso’s Invalidity Pension Scheme (IPS): an Actuarial Present Value (APV) modelling,” Universiti Teknologi MARA, 2017. [Google Scholar] [Crossref]

4. M. Z. Awang Chek and I. L. Ismail, “Maximizing Retirement Savings: Strategic Forecasting of Employees’ Provident Fund (EPF) Dividends,” Int. J. Res. Innov. Soc. Sci., 2024. [Google Scholar] [Crossref]

5. Social Security Organization (SOCSO) Malaysia, “Gig Workers Bill and Informal Sector Coverage Initiatives,” 2024. [Google Scholar] [Crossref]

6. H. Awang, “Factors related to successful return to work following multidisciplinary rehabilitation,” J. Rehabil. Med., 2017. [Google Scholar] [Crossref]

7. H. Zacher and B. Griffin, “Older workers’ age as a moderator of the relationship between proactive personality and career competence,” 2015. [Google Scholar] [Crossref]

8. Radzuan and others, “Forecasting road accidents using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Networks,” JSSM, 2020. [Google Scholar] [Crossref]

9. OECD, “Pensions at a Glance 2025: Key findings and policy implications,” 2025. [Google Scholar] [Crossref]

10. I. L. Ismail, M. Zaki, A. Chek, and M. Syakir, “Understanding the Employment Insurance Scheme in Malaysia,” vol. 13, no. 11, pp. 2137–2143, 2023, doi: 10.6007/IJARBSS/v13-i11/19622. [Google Scholar] [Crossref]

11. P. C. Ferreira and S. de A. Pessôa, “The effects of longevity and distortions on education and retirement,” Rev. Econ. Dyn., vol. 10, no. 3, pp. 472–493, 2007, doi: 10.1016/j.red.2007.01.003. [Google Scholar] [Crossref]

12. M. Z. A. Chek, I. L. Ismail, and N. F. Jamal, “Optimising Contribution Rate for SOCSO’s Invalidity Pension Scheme: Actuarial Present Value (APV),” Int. J. Eng. Technol., vol. 7, pp. 83–92, 2018, doi: 10.14419/ijet.v7i4.33.23491. [Google Scholar] [Crossref]

13. T. A. Luehrman, “Using APV (adjusted present value): a better tool for valuing operations.,” Harv. Bus. Rev., vol. 75, no. 3, 1997. [Google Scholar] [Crossref]

14. S. Iyer, “Social insurance pension schemes: Stochastic actuarial valuation using an analytical model,” Asia-Pacific Journal of Risk and Insurance, 2015. http://hy9th9qv9w.search.serialssolutions.com/?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Social+Insurance+Pension+Schemes:+Stoch (accessed Jan. 19, 2016). [Google Scholar] [Crossref]

15. Nurulhuda Binti Jamaluddin, Ho Jen Sim, Akmalia Shabadin, Nusayba Megat Johari, and Wahida Ameer Batcha, “Exposure Work Commuting: Case Study among Commuting Accidents in Klang Valley, Malaysia,” J. Civ. Eng. Archit., 2015, doi: 10.17265/1934-7359/2015.01.006. [Google Scholar] [Crossref]

16. K. Syuhada and others, “Expectile-based Neural Network Approach for Mixed-Frequency Economic Forecasting,” 2023. [Google Scholar] [Crossref]

17. J. Reich, “Rebooting MOOC research,” Science (80-. )., vol. 347, no. 6217, pp. 34–35, 2015. [Google Scholar] [Crossref]

18. U. Sivarajah, M. M. Kamal, Z. Irani, and V. Weerakkody, “Critical analysis of Big Data challenges and analytical methods,” J. Bus. Res., vol. 70, pp. 263–286, 2017, doi: 10.1016/j.jbusres.2016.08.001. [Google Scholar] [Crossref]

19. F. M. Liou, Y. C. Tang, and J. Y. Chen, “Detecting hospital fraud and claim abuse through diabetic outpatient services,” Health Care Manag. Sci., 2008, doi: 10.1007/s10729-008-9054-y. [Google Scholar] [Crossref]

20. T. E. Dalkilic, F. Tank, and K. S. Kula, “Neural networks approach for determining total claim amounts in insurance,” Insur. Math. Econ., vol. 45, no. 2, pp. 236–241, 2009, doi: 10.1016/j.insmatheco.2009.06.004. [Google Scholar] [Crossref]

21. H. K. Teoh, A. B. Abdullah, and W. C. Yap, “Performance Evaluation of Artificial Neural Networks in Predicting RTW Outcomes: A Case Study of SOCSO,” J. Occup. Rehabil., vol. 40, no. 4, pp. 456–469, 2023. [Google Scholar] [Crossref]

22. M. Z. A. Chek, I. L. Ismail, H. Hasim, and A. F. Mansor, “Profiling Return – to – Work (RTW) Recipients in Malaysia,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 12, no. 7, pp. 925–935, 2022. [Google Scholar] [Crossref]

23. M. Z. A. Chek, T. P. Leong, M. H. Abdul Halim, and I. L. Ismail, “Understanding The Impact of Covid-19 Outbreak in Malaysia,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 12, no. 7, pp. 936–943, 2022, doi: 10.6007/ijarbss/v12-i7/14323. [Google Scholar] [Crossref]

24. M. Z. Awang Chek and I. L. Ismail, “UNDERSTANDING FACTORS AFFECTING CONTRIBUTION RATE OF SOCIAL INSURANCE,” Int. J. Soc. Sci. Manag. Rev., no. October, pp. 121–128, 2022, [Online]. Available: www.ijssmr.org [Google Scholar] [Crossref]

25. M. Z. A. M. Z. A. Chek, I. L. I. L. Ismail, and N. F. N. F. Jamal, “Descriptive research on SOCSO’s Invalidity Pension Scheme (IPS) claims payment,” Int. J. Recent Technol. Eng., vol. 8, no. 2 Special Issue 11, pp. 660–663, 2019, doi: 10.35940/ijrte.B1104.0982S1119. [Google Scholar] [Crossref]

26. S. Hassan, Z. Othman, and M. S. Hashim, “Savings for retirement in the Employees Provident Fund (EPF): A profile of contributors and their views towards the EPF Scheme,” Curr. Perspect. to Econ. Manag., vol. 2, pp. 60–70, 2018. [Google Scholar] [Crossref]

27. M. A. Aziz Mohammed, “The Return to Work Programme in Malaysia - investing in people,” Int. J. Disabil. Manag., 2014, doi: 10.1017/idm.2014.8. [Google Scholar] [Crossref]

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