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

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.