Abstract:
This study investigates the potential of Artificial Intelligence (AI) technologies to enhance the distribution and allocation of social grants in South Africa, addressing inefficiencies, high costs, and administrative complexities. Social grants are vital for reducing poverty and improving welfare, but the current system faces challenges such as lengthy processing times, inconsistent data, and limited accessibility. A qualitative research design was employed, utilising semi-structured interviews with 20 stakeholders, including policymakers, officials from the South African Social Security Agency (SASSA), and beneficiaries. Data analysis used thematic analysis to assess the feasibility of AI integration. Pilot studies of Electronic Know Your Client (EKYC) and biometric verification across select provinces provided empirical insights.
In order to enhance decision-making, lower fraud, expedite beneficiary verification, and optimise resource allocation within South Africa's social grant ecosystem, this study explores the potential applications of artificial intelligence (AI) technologies, including machine learning, predictive analytics, natural language processing, automated document verification, and anomaly detection. The study looks at international best practices, assesses the possible advantages and disadvantages of integrating AI in South Africa, and reviews policy preparedness in light of the nation's larger Fourth Industrial Revolution (4IR) goal.
Findings reveal that AI technologies could mitigate inefficiencies by automating data verification, enhancing fraud detection, and streamlining decision-making processes. Pilot implementations of Electronic Know Your Client (EKYC) and biometric systems across Gauteng, Eastern Cape, and North West provinces demonstrated varied outcomes. Gauteng saw a 5% improvement in fraud detection but faced challenges with system integration. The Eastern Cape struggled with downtimes, while the North West achieved a 10% reduction in identity theft but encountered transaction inaccuracies.
This research contributes a comprehensive AI integration model tailored to South Africa’s socio-economic context, providing actionable recommendations for policymakers. Key considerations include robust infrastructure, workforce training, regulatory updates, and ethical safeguards to ensure fair and inclusive AI deployment. The study advances the discourse on AI in public administration,
offering practical insights for achieving greater efficiency, transparency, and equity in social grant distribution.
