AI-DRIVEN CREDIT SCORING, FINANCIAL INCLUSION, AND SME FINANCING PERFORMANCE IN PAKISTAN

Abstract

Artificial Intelligence (AI) has transformed financial services by enabling more efficient, accurate, and data-driven credit assessment processes. In emerging economies such as Pakistan, where small and medium-sized enterprises (SMEs) continue to experience significant financing constraints due to information asymmetry, limited credit histories, and collateral requirements, AI-driven credit scoring presents a promising solution for enhancing financial inclusion and improving financing outcomes. This study examined the influence of AI-driven credit scoring on SME financing performance, with financial inclusion serving as a mediating variable and digital financial infrastructure and regulatory support acting as moderating variables. A quantitative, cross-sectional research design was employed, and primary data were collected from SME owners and managers operating across Pakistan using a structured questionnaire. The proposed conceptual framework was empirically tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicated that AI-driven credit scoring significantly enhanced financial inclusion and SME financing performance. Financial inclusion partially mediated the relationship between AI-driven credit scoring and financing performance, while digital financial infrastructure and regulatory support strengthened the proposed relationships. The study contributes to the literature by extending Information Asymmetry Theory within the context of AI-enabled financial services and provides practical and policy implications for financial institutions, fintech companies, and regulators seeking to promote responsible AI adoption and inclusive SME financing. The findings offer valuable insights for advancing Pakistan's digital financial ecosystem and supporting sustainable economic development through technology-driven financial innovation.