Agent-based mapping of credit risk for sustainable microfinance

TL;DR

This paper introduces an agent-based network model to analyze microfinance credit risk, revealing how economic deterioration and adverse selection can cause regime shifts in repayment rates, with implications for risk management.

Contribution

It presents a novel agent-based framework using network theory to map microfinance credit risk and demonstrates its application with real-world data from Grameen Bank.

Findings

Latent moral hazard can trigger regime shifts in repayment probability.

Economic improvement beyond the initial deterioration is needed for recovery.

Measurable quantities can help balance loan pricing and operational sustainability.

Abstract

Inspired by recent ideas on how the analysis of complex financial risks can benefit from analogies with independent research areas, we propose an unorthodox framework for mapping microfinance credit risk---a major obstacle to the sustainability of lenders outreaching to the poor. Specifically, using the elements of network theory, we constructed an agent-based model that obeys the stylised rules of microfinance industry. We found that in a deteriorating economic environment confounded with adverse selection, a form of latent moral hazard may cause a regime shift from a high to a low loan repayment probability. An after-the-fact recovery, when possible, required the economic environment to improve beyond that which led to the shift in the first place. These findings suggest a small set of measurable quantities for mapping microfinance credit risk and, consequently, for balancing the requirements to reasonably price loans and to operate on a fully self-financed basis. We illustrate how the proposed mapping works using a 10-year monthly data set from one of the best-known microfinance representatives, Grameen Bank in Bangladesh. Finally, we discuss an entirely new perspective for managing microfinance credit risk based on enticing spontaneous cooperation by building social capital.