Anticipating the Difficulties of Microfinance Institutions: An Early Warning Model Applied to the Togolese Context

Abstract

The Decentralized financial system (DFS) is a sector in growth in developing countries. It offers financial services to actors rejected by formal banks because of their risk. therefore, a strict monitoring of this sector is needed to anticipate and avoid the bankruptcies of DFSs. In this context, an early warning model is a feasible solution. The aim of this paper is to develop a model of alert capable of predicting the difficulties of microfinance institutions. Data processed from 49 DFSs over 5 years led to a model explaining up to 80% of the likelihood of bankruptcy. Statistically positive results show that any increase in the portfolio at risk, the provisions on outstanding loans and the provisions on total assets leads to an increase in the probability of difficulty for the DFSs, thus increasing its insolvency risk while any increase in the equity risk coverage ratio, the operating income on owner's equity, the loan coverage by deposits ratio, the profit margin and the outstanding deposits on total assets leads to a decrease in the probability of default for the DFSs.