Leveraging Artificial Intelligence and Machine Learning to Reform India's Social Protection Framework

This study explores the application of Artificial Intelligence (AI) and Machine Learning (ML) to enhance the integrity and efficiency of India's Social Protection and Direct Benefit Transfer (DBT) systems. With increasing fiscal leakage and errors in identifying beneficiaries, AI offers a transformative solution for detecting fraud, improving targeting, and ensuring that benefits are delivered to the right individuals. Data was collected through structured surveys and expert interviews in rural areas of Bihar and Telangana, as well as anonymized transaction data from DBT initiatives. The research employed classification and anomaly detection models, revealing that AI can reduce false beneficiary inclusion by up to 27% and accurately forecast fraudulent transaction patterns with a 91% success rate. This paper promotes the integration of AI technologies with Aadhaar, eKYC, and program Management Information Systems (MIS) for real-time validation. It contributes both empirical and methodological insights to the realm of digital governance and offers valuable perspectives for developing countries aiming for inclusive and effective welfare delivery.