Leveraging Biometric Data and Artificial Intelligence to Enhance Beneficiary Identification in Social Cash Transfer Programs: A Case Study of Crystalised Applications in Zambia

Abstract

This paper investigates the integration of biometric data and artificial intelligence (AI) to improve beneficiary identification within Social Cash Transfer (SCT) programs in Zambia. Focusing on the Crystalized Apps platform, the research examines how AI-driven biometric technologies, such as facial recognition, fingerprint scanning, and iris detection, significantly enhances accuracy, operational efficiency, and security of SCT disbursements. Utilizing a mixed-methods approach, the study combines interviews with program administrators and an analysis of transaction data to evaluate the effectiveness of AI-enhanced biometric systems in beneficiary verification, fraud reduction, and payment security. The anticipated results aim to demonstrate that biometric AI can mitigate identity-related fraud, optimize the transfer process, and promote transparency within the system. The study also acknowledges challenges including data privacy, infrastructure limitations, and digital literacy gaps, providing a holistic perspective. Ultimately, this research seeks to provide valuable insights on how AI-based biometric authentication can strengthen social protection mechanisms, improve financial inclusion, and foster greater accountability in public welfare programs.