Modeling the Propensity to Default on Microloans in Mali, Africa

Abstract

Microfinance is a global phenomenon that is focused on sustainable poverty alleviation.    By providing people in developing countries with the capital to sustain themselves and an educational background on which to build their futures, microfinance institutions (MFIs) have given the poor an opportunity to get out of poverty.  For the purposes of this study, a specific MFI in Mali Africa was utilized to model the propensity for micro-borrowers to default on their loans.  Using the MFI's historical data on each of their loans, this study models the repayment percentage of individual loans, contingent upon qualitative and quantitative factors.   Employing an Ordinary Least Squares Model I am able to analyze how each independent factor influences default rates.  I also harness fuzzy analysis to group together factors that contribute to high default rates. I hypothesize that high default rates were encouraged by a longer time between payments, a large initial loan size, business development in investment heavy industries, and starting a business in a hostile market environment.   By utilizing these results, the MFI can optimize its loan repayment success by targeting specific borrowers and modifying their loan structure. The purpose of this study is to provide the Mali MFI with tangible results that they can utilize to increase their loaning effectiveness.   This model is important because microfinance is a relatively new field and it seeks to improve the Mali MFI's poverty alleviating capacity.