Anila Kathi, Mandara Raj J P

Keywords: AI-Driven Welfare, Blockchain Transparency, Big Data Personalization, Social Security,  Innovation, Fraud Prevention, Digital Inclusion, Ethical AI Frameworks, Real-Time Analytics, Welfare Efficiency, Data Privacy.

INTRODUCTION

The convergence of Artificial Intelligence (AI), Blockchain, and Big Data is reshaping global welfare systems, offering unprecedented opportunities to enhance targeting, reduce exclusion errors, and ensure transparency. AI-driven welfare programs, like Brazil’s Family Grant and India’s AI for 2030 initiative, are optimizing resource allocation and fraud detection. Blockchain technology, exemplified by the World Food Program’s Building Blocks initiative, is fostering trust and efficiency in welfare distribution. Meanwhile, Big Data is enabling personalized welfare solutions, as seen in Kenya’s Hunger Safety Net Program and India’s Aadhaar-linked systems. However, challenges like algorithmic bias, data privacy concerns, and digital literacy gaps persist. By addressing these through ethical frameworks, robust regulations, and public-private partnerships, governments can harness these technologies to create inclusive, efficient, and corruption-free welfare systems. This article explores how AI, Blockchain, and Big Data are revolutionizing social security, offering innovative insights into the future of welfare delivery.

Theme 1: AI-Driven Welfare: Enhancing Targeting and Reducing Exclusion Errors

Artificial Intelligence (AI) is transforming welfare delivery mechanisms (Nayak et al., 2024) globally by enhancing beneficiary targeting, reducing errors and preventing fraud. While conventional welfare programs majorly rely on static eligibility criteria and periodic assessments, which can result in the exclusion of dynamically changing vulnerable populations, AI facilities continuously monitor and adaptive adjustments, allowing for a more responsive and inclusive welfare distribution (Criado et al., 2017).

Globally there are many countries which have integrated AI into welfare programs to improve efficiency. For instance, Brazil’s Family Grant Program has integrated AI since 2024 to enhance beneficiary verification, ensuring financial inclusivity, while preventing fraud through extensive data analysis (Folha de S. Paulo, 2024). With AI analyzing over 1.3 petabytes of information, the program optimizes resource allocation and strengthens transparency in social assistance initiatives (Folha PE, 2024). Similarly, Australia’s social security system has integrated AI to enhance service delivery, fraud detection and debt recovery, with Centrelink leveraging AI models to identify high-risk claims and optimize staff allocation (ACS, 2025).

Similarly to global welfare schemes, India is actively integrating AI into its social security and welfare mechanisms to enhance service delivery and inclusivity (Marda, 2018). One such instance is the AI for India 2030 initiative, launched in collaboration with MEITY, which focuses on leveraging AI to transform agriculture, healthcare, and urban planning (Kaushik et al., 2025). In partnership with Google Cloud, Axis My India has developed an AI-powered multilingual super-app to raise awareness about government social welfare schemes (Public policy Google., 2024).

Despite these advantages, AI-driven welfare programs face challenges such as algorithmic bias, lack of digital literacy, and concerns over data privacy (Verma et al., 2024). To mitigate these risks, governments must establish transparent AI regulations, invest in digital literacy programs, and create ethical AI frameworks (Verma et al., 2024).

Theme 2: Blockchain for Transparent and Corruption-Free Welfare Systems

Blockchain technology is emerging as a powerful tool to enhance transparency and efficiency in social welfare programs. Blockchain technology can make welfare programs more transparent and efficient, building trust and ensuring that the right people get the help they need (Tang et al., 2022). Across the globe, several countries have successfully implemented blockchain-based welfare initiatives, demonstrating its potential to improve social protection systems (Kim et al., 2019).

One of the prominent examples is, the World Food Program’s (WFP) Building Blocks initiative in Jordan which use blockchain technology. It leverages Ethereum-based blockchain to facilitate cash-based transfers for Syrian refugees in Jordanian camps like Azraq (UNWFP., 2017). It ensures secure, transparent, and efficient transfers while reducing banking fees and enhancing data protection (UNWFP, 2017). Similarly, Nigeria’s Growth Enhancement Support Scheme (GESS) leverages blockchain to distribute agricultural subsidies directly to farmers, preventing corruption in fertilizer distribution (Okolo-Obasi et al., 2019).

India is exploring the integration of blockchain technology into its social security and welfare programs to enhance transparency, efficiency, and trust (Singh et al., 2025). While full-scale implementation is still underway, several initiatives highlight this trend. For instance, Ayushman Bharat Digital Mission, a flagship health program aims to create a comprehensive digital health ecosystem. Blockchain technology is being considered to secure and manage electronic health records (EHRs), ensuring data integrity and reducing fraudulent claims (Garg et al.,2025). And National Blockchain Framework (NBF), launched by MEITY, offers Blockchain-as-a-Service (BaaS) through the VISHVASYA-Blockchain Technology Stack (Singh et al., 2025). This infrastructure supports various permissioned blockchain applications, including those in the social security sector, aiming to improve service delivery and reduce administrative overhead (PIB, 2024).

Despite many advantages of integrating Blockchain Technology in social security programs, there exist limitations as well. Such as high infrastructure costs and the need for a robust digital ecosystem, pose significant barriers to widespread adoption. And it requires technical expertise and strong government commitment, which many developing nations lack. Third, limited digital literacy among beneficiaries, particularly in rural areas, may hinder accessibility and user adoption (Sachan, 2018). To address this, solutions such as public-private partnerships (PPPs), can play a crucial role in funding and implementing blockchain infrastructure for welfare programs (Cole, 2024).

Theme 3: Big Data and Welfare Personalization: The Future of Social Security

Big data is transforming the landscape of social welfare delivery systems, by enabling personalized and data-driven policymaking (Charlotte et al., 2019). Traditional welfare programs often rely on outdated census data and generalized eligibility criteria, leading to exclusion errors and inefficient resource allocation (US GAO, 2024). Big data, when combined with real-time analytics, GIS mapping, and machine learning, offers an opportunity to tailor social protection programs to individual needs, predict economic distress, and optimize policy interventions (Kumar et al., 2025).

Global welfare schemes such as Kenya’s Hunger Safety Net Program (HSNP) exemplify the application of big data in social welfare by predicting food shortages and triggering cash transfers in drought-prone regions (FAO, 2016). Similarly, China’s Social Credit System (SCS) exemplifies the use of big data in governance by collecting and analyzing vast amounts of information to monitor and influence the behaviour of citizens and enterprises. This data-driven approach aims to enhance trustworthiness and compliance within society (MERICS, 2019).  

India as well as using big data through systems such as the Aadhaar-Enabled Public Distribution System (PDS) links biometric identity verification to subsidy distribution, reducing fraud but raising concerns about data privacy (Nilekani, 2024). Similarly, Telangana’s SAMAGRA Vedika system integrates multi-departmental data for targeted service delivery, though it also raises surveillance concerns (Amnesty International, 2024). In agriculture, the Rythu Bharosa Scheme uses big data to ensure timely financial aid for farmers, though biometric mismatches sometimes cause exclusions (Masiero & Buddha, 2021). These initiatives highlight India’s commitment to data-driven governance, while also emphasizing the need to address privacy and inclusion challenges.

Despite its potential, big data-driven welfare systems face key challenges. Such as data privacy concerns, algorithmic bias in data models and limited digital infrastructure in developing nations hinder the widespread adoption of real-time data analytics for welfare personalization (Nir, 2014). However, strong regulatory frameworks, robust data protection laws, and ethical AI practices are essential to prevent misuse.

ADDITIONAL INSIGHTS

The integration of AI, Blockchain, and Big Data into welfare systems is not just a technological upgrade but a paradigm shift in governance. AI’s ability to analyze vast datasets in real-time allows for dynamic beneficiary targeting, reducing exclusion errors and ensuring aid reaches the most vulnerable. For instance, Brazil’s AI-powered Family Grant Program has optimized resource allocation by analyzing over 1.3 petabytes of data, setting a global benchmark. Blockchain, with its decentralized and immutable nature, is eliminating corruption and enhancing transparency. The World Food Program’s Ethereum-based system in Jordan has revolutionized cash transfers for refugees, reducing costs and ensuring data security. Big Data, on the other hand, is enabling predictive analytics, as seen in Kenya’s Hunger Safety Net Program, which anticipates food shortages and triggers timely interventions. India’s Aadhaar-linked systems and Telangana’s SAMAGRA Vedika are leveraging Big Data for targeted service delivery, though privacy concerns remain. To fully realize the potential of these technologies, governments must invest in digital literacy, establish ethical AI frameworks, and foster public-private partnerships. These innovations are not just transforming welfare systems but are also redefining the social contract between citizens and the state, paving the way for a more equitable and efficient future.

CONCLUSION

The fusion of AI, Blockchain, and Big Data is revolutionizing welfare systems, offering smarter, more inclusive, and transparent solutions. From Brazil’s AI-driven Family Grant to Jordan’s blockchain-based cash transfers, these technologies are enhancing efficiency, reducing fraud, and ensuring aid reaches the most vulnerable. However, challenges like algorithmic bias, data privacy, and digital literacy gaps must be addressed through robust regulations, ethical frameworks, and public-private collaborations. As governments worldwide embrace these innovations, the potential for creating equitable and corruption-free welfare systems becomes increasingly attainable. By leveraging AI for dynamic targeting, Blockchain for transparency, and Big Data for personalization, we can build a future where social security is not just a safety net but a springboard for growth and inclusion. The journey towards a smarter welfare system is complex, but with the right policies and partnerships, it is undoubtedly within reach.

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