Computational Financial Inclusion Using Machine Learning Techniques: Relationship between Outreach and Usage

Abstract

Financial inclusion is developing as a global standard for measuring each country’s economic sustainability and growth. The indicators of financial inclusion include the physical accessibility of financial services (outreach) and the people’s use of them (usage). To use the indicators in policy making and development planning, the following question needs to be addressed: If a new development is designed to improve the state of the outreach, what changes can be expected in the usage as a result? This paper excavates this outreach-usage relationship from the International Monetary Fund’s Financial Access Survey data as well as the World Bank Global Findex data using machine learning techniques. The results are compared with the Composite Index scores for confirmation in the correlation.