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Inspired by recent ideas on how the analysis of complex financial risks canbenefit from analogies with independent research areas, we propose anunorthodox framework for mapping microfinance credit risk---a major obstacle tothe sustainability of lenders outreaching to the poor. Specifically, using theelements of network theory, we constructed an agent-based model that obeys thestylised rules of microfinance industry. We found that in a deterioratingeconomic environment confounded with adverse selection, a form of latent moralhazard may cause a regime shift from a high to a low loan repaymentprobability. An after-the-fact recovery, when possible, required the economicenvironment to improve beyond that which led to the shift in the first place.These findings suggest a small set of measurable quantities for mappingmicrofinance credit risk and, consequently, for balancing the requirements toreasonably price loans and to operate on a fully self-financed basis. Weillustrate how the proposed mapping works using a 10-year monthly data set fromone of the best-known microfinance representatives, Grameen Bank in Bangladesh.Finally, we discuss an entirely new perspective for managing microfinancecredit risk based on enticing spontaneous cooperation by building socialcapital.

Agent-based mapping of credit risk for sustainable microfinance