Leveraging Digital Technologies for Financial Inclusion: An Empirical Analysis of SME Financing in Emerging Economies

Abstract

While digital financial technologies (DFTs) are spreading faster in emerging markets the proof is limited to the mechanisms that make AI-based credit tools increasingly efficient at improving affordability for most people and enabling regulatory environments. This study uses a Partial Least Square Structural Equation Modelling (PLS-SEM) method which is available in SmartPLS 4 software and contains data involving 150 experts in the banking sector, executives in the FinTech sector, credit analysts, and financial advisors of SMEs in emerging economies, selected purposefully for only this study. The measurement model showed great dependability and accuracy for each construct (Cronbach's Alpha: 0.843–0.901; Composite Reliability: 0.863–0.925; Average Variance Extracted: 0.564–0.713) and HTMT ratios were consistently below 0.20, confirming high discriminant validity. The results of the structural model showed that the direct path from R1 to R2 was negative (β = -0.196, R² = 0.038), as was the direct path from R2 to R3 (β = -0.071, R² = 0.005), this transpired in a positive indirect path from R1 to R3 (β = 0.014), exactly what we see happening with AI in hard to enter situation where the scale of roll out and barrier awareness are positively related. Model fit indices were acceptable (SRMR = 0.064, NFI = 0.866). A post-hoc power analysis revealed that this pathway (R2→R3) had a critical underpowering (achieved power = 22.0%). This enabled me to understand it was suggesting that 1,220 respondents were a minimum needed to make a solid conclusion about the relationship between these two variables. The study gives a backed up three-construct measurement framework for fintech access research and shows we should expand the study internationally. The best way to do that would be using the longitudinal study design to figure out the real path AI finance adoption, easier access, and shifting regulations in emerging economies.