Predicting financial inclusion and access to credit in Kenya
Date
2024
Authors
Tanui, C.
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Publisher
Strathmore University
Abstract
Financial inclusion, particularly access to credit, is a crucial aspect of economic development in Kenya. This study aims to investigate the determinants of financial inclusion and access to credit in Kenya, employing logistic regression modeling to predict financial inclusion patterns; and construct a forecast model that can support policymakers and financial organizations in boosting financial inclusion. The study analyzed several factors including demographics, technology adoption, financial services usage and barriers to assess their impact on financial inclusion and access to credit. The results revealed that the use of mobile phones and the internet as technological indicators of financial inclusion were the most effective predictors. Contrary to previous studies, gender was not found to significantly affect financial inclusion in this context. The development of a machine-learning model achieved an overall prediction accuracy of 90.9%. An interactive user dashboard was also developed using flexdashboard in R and hosted in the web, with visualizations and regression models to provide insights into the key factors driving financial access in Kenya. The results showed that demographics, technology adoption, financial services usage and barriers to financial inclusion were the most significant factors that impacted financial inclusion; however, there were no significant correlations between these factors and financial inclusion as a whole. This research study will offer insights into the causes of financial exclusion in the country and how to overcome them.
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