Transforming Financial Access Through AI: An Empirical Study in Pakistan

Abstract

Artificial Intelligence (AI) integration in emerging economies like Pakistan presents a transformative opportunity to advance financial inclusion. This study investigates the influence of AI on financial inclusion through key mediating variables—financial literacy, user adoption, financial behavior practices, government support, and AI-based risk mitigation—using a quantitative methodology. Data from 250 financial service users were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that financial literacy (β = 0.558, p < 0.001) and user adoption (β = 0.368, p < 0.001) significantly mediate the relationship between AI and financial inclusion, with financial literacy showing the strongest effect. In contrast, financial behavior practices (p = 0.250), government support (p = 0.268), and risk mitigation (p = 0.111) were found to have no significant mediating influence. Theoretically, the study extends existing literature by integrating the Technology Acceptance Model (TAM), Financial Inclusion Theory, Diffusion of Innovation Theory, and Social Cognitive Theory, offering a multidimensional understanding of how AI adoption interacts with user behavior and systemic access. It challenges conventional assumptions about the sufficiency of institutional support, emphasizing instead the centrality of financial literacy and user readiness in AI-driven inclusion. Methodologically, the study demonstrates the value of PLS-SEM in validating complex structural models with multiple latent constructs in emerging market contexts. The results offer valuable insights for researchers and policymakers aiming to design AI-enabled strategies that improve financial literacy, boost user adoption, and foster inclusive financial ecosystems.

Keywords: Artificial Intelligence, Financial Inclusion, Financial Literacy, User adoption, AI-Based Risk Mitigation, Financial Behavior Practices, Government Support.