Ai-Driven Data Governance for Transparent and Fair Student Loan Allocation in Public Institutions

Abstract

Student lending in public institutions is a fundamental issue that has continued to pose a challenge to the policy frameworks of developing countries in general and Nigeria in particular. This is in relation to the matter of transparency, equity, and integrity. Currently, the new initiatives that have been undertaken in Nigeria to ensure wider access to education have heightened the need to develop governance frameworks that will ensure equity in the allocation of student loans. This study adopts a quantitative research design and employs a simulated but empirically analysed dataset constructed to reflect real-world conditions in Nigeria’s public student loan system. Data were obtained from 578 valid respondents drawn from public higher education institutions and government agencies involved in student loan administration.  The results indicate that stakeholder trust, transparency, risk and fraud management, and fairness in decision-making are positively influenced by AI-based data governance. Stakeholder trust, transparency, and risk and fraud management are identified as the key drivers of accurate student loan distribution. The implications from the results are that AI governance frameworks improve student loan disbursement not only because they are efficiency-driven but also because they are linked to higher levels of legitimacy. It is an important contribution to the body of research on AI governance in public finance because this study illustrates how socio-technical AI governance can be used to improve equity in student loan programs. From a premise concerning public policy, this research work is an important consideration for policymakers because it provides an indication that AI should be used to improve equitable and sustainable student financing for higher education in Nigeria and similar developing nation-states.