Investigating the drivers of financial inclusion & MSME performance using machine learning techniques and statistical models

Abstract

In this study, I investigated the relationship between financial inclusion levels (FI) and business performance levels among Micro, Small, and Medium Enterprises (MSMEs). Particular attention was paid to the impact that business characteristics have on financial inclusion, business performance and the relationship between them. As much as there are global efforts to improve financial inclusion, questions remain about its actual impact on business outcomes, especially in low- and middle-income countries. To address this, I employed a mixed-methods approach, integrating advanced machine learning techniques and statistical testing to ensure that both linear relationships and complex, non-linear patterns are uncovered in the data sourced from six different countries. My findings confirm a positive and statistically significant association between financial inclusion and business performance, while also revealing that certain business characteristics and resources are critical enablers of both inclusion and performance across the six countries. The study concludes that while financial inclusion is worth the attention it commands among policymakers and researchers, it must be integrated with capacity-building innovations to unlock its full potential to harness MSME performance. The findings in this study provide actionable evidence for policymakers, financial institutions, and development agencies aiming to design targeted, inclusive, and sustainable business support programs.