Descriptive Analytics of Frequency Occupational Fatalities among Socso’s Contributors

M. Z. A. Chek, I. L. Ismail, E. N. I. Hashim, M. Shah and M. A. A. A. Aziz
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M. Z. A. Chek: Actuarial Science Department, UiTM Perak Branch
I. L. Ismail: Department of Statistics and Decision Science, UiTM Perak Branch
E. N. I. Hashim: Actuarial Science Department, UiTM N. Sembilan Branch
M. Shah: Social Security Organisation (SOCSO), Malaysia
M. A. A. A. Aziz: Social Security Organisation (SOCSO), Malaysia

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 14, 715-722

Abstract: This study presents a comprehensive descriptive analysis of the frequency of claims for SOCSO’s Dependents’ Benefit spanning the years 1972 to 2023. The data set comprises 52 years of recorded claims, highlighting a substantial growth trend from 76 claims in 1972 to 383,101 claims in 2023. The study aims to analyze trends in claim frequencies, assess statistical distributions, and provide insights into the driving factors behind the increasing claim counts. A range of descriptive statistical measures, including the mean (125,347), standard deviation (131,239), variance, and distributional skewness, were applied to interpret claim fluctuations over time. The findings indicate a consistent upward trend in claim frequency, driven by socio-economic factors, legislative amendments, and workforce demographic shifts. Graphical representations, such as time-series plots and histograms, further support the observed trends and their implications. The study recommends periodic policy evaluations, enhancements in contribution structures, and data-driven financial planning to secure the long-term viability of the SOCSO Dependent Benefit scheme. Future research should explore advanced machine-learning models and actuarial simulations to improve predictive accuracy and policy responsiveness.

Date: 2025
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