In an era of digital transformation, public welfare systems across the globe are undergoing significant evolution. Traditional subsidy models have long struggled with challenges like inaccurate targeting, delayed disbursements, and leakages due to human error or corruption. However, the adoption of artificial intelligence (AI) and big data technologies is ushering in a new wave of efficiency, accountability, and accessibility in public assistance programs.
A practical example of this transformation can be seen in Pakistan’s social welfare sector. Initiatives like the Ehsaas and Benazir Income Support Programme (BISP) have embraced digitization to improve their outreach. Citizens can now verify their eligibility and apply for benefits through digital platforms such as the 8171 Ehsas Program BISP CNIC check by PhoneWorld, a tool designed to streamline public engagement and transparency. This system leverages national identity data, socio-economic profiling, and real-time monitoring—setting a precedent for other developing nations.
The integration of such technologies into government welfare schemes is not merely a regional trend but part of a larger global movement toward data-informed governance.
AI’s ability to identify eligible individuals for subsidy programs more accurately than manual systems is a game-changer. With access to datasets covering income levels, geographic locations, household size, and even behavioral patterns, AI models can profile applicants and allocate aid with greater precision.
For instance, predictive analytics allows governments to create beneficiary heatmaps or poverty indices using data from mobile devices, banking behavior, or census results. Machine learning models can then rank applications or trigger automatic approvals for high-need cases—saving time and reducing exclusion errors.
Programs like Ehsaas use AI to screen CNIC data and cross-reference it with financial, health, and employment databases to ensure assistance reaches only the most deserving.
The second major advantage of AI and big data is enhanced governance through real-time analytics. Fraud, duplicate entries, and ghost beneficiaries have plagued public welfare programs for decades. Big data platforms, when paired with machine learning, can monitor transactions, flag suspicious activity, and perform identity verification using biometric data.
Pakistan’s BISP has started implementing biometric verification at distribution points, and data anomalies are tracked through centralized dashboards. Globally, governments are using similar systems to improve transparency—providing visibility across supply chains, regional disbursements, and demographic impact.
Furthermore, many platforms now include automated grievance redressal mechanisms. Natural language processing (NLP) tools analyze complaint submissions to detect trends and identify areas of concern—helping administrators respond faster and allocate resources more effectively.
AI also enables a shift from one-size-fits-all subsidy models to more personalized assistance. For example, a dynamic system may allocate additional funds to families with young children, elderly members, or those living in high-cost regions. Some models even adjust aid based on fluctuating inflation rates or real-time disaster impact assessments.
Inclusion also improves through digital interfaces such as mobile apps, SMS services, and IVR systems. These tools reduce travel burdens, minimize paperwork, and make services accessible even in remote areas.
The convergence of AI, big data, and digital platforms is redefining how public subsidy programs operate. These technologies not only increase efficiency but also promote fairness, transparency, and inclusivity at scale. Moving forward, countries must focus on building robust digital infrastructures, protecting data privacy, and ensuring algorithmic fairness to maximize the potential of AI in public welfare. With strategic implementation and global collaboration, digital innovation can help governments build smarter, more equitable social safety nets for the future.