In September 2019, Daung Capital (Daung), a microfinance institution (MFI) in Myanmar, was preparing to launch a new loan product for farmers which entailed a lump sum repayment instead of monthly repayments like its other products. Leon Qiu, the co-founder of Daung, wondered how he could implement an appropriate credit risk model for the new product.
Credit Risk was particularly high in the MFI sector in Myanmar due to the absence of a Credit Bureau and lack of information sharing amongst MFI providers which indirectly encouraged borrowers to take multiple loans from different providers leading to over-indebtedness.
Daung had implemented a rent to own model in collaboration with dealers to provide loans to borrowers for purchasing motorcycles under a monthly repayment scheme. Similarly, the company had launched a loan scheme for factory workers in partnership with factories and small businesses, deducting the monthly repayment directly from the workers' salary to reduce credit risk. For granting loans to farmers, the company had partnered with rice mills to minimise credit risk.
Qiu was unsure if these measures were sufficient and ruminated on how to create the perfect balance between managing risk and generating value in microfinance. How could Daung mitigate the credit risks of loan products designed for low-income populace? What risk management processes could it implement to ensure the sustainability of its business? Could technologies like data analytics and machine learning hold the panacea for credit risk in microfinance?
The case illustrates the various challenges faced by an MFI in assessing credit risk of borrowers. It helps students understand the dynamics of the MFI sector in Myanmar and credit risks presented by different stakeholders in a predominantly rural economy. The case is designed for undergraduate, postgraduate and executive education students in risk management, financial services, microfinance in emerging markets and accounting data analytics classes.
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SMU Faculty/Staff can download the case & teaching note on iNet with your SMU login ID & Password via the following links:
For purchase of the case and supplementary materials via The Case Centre, please access the following links:
For purchase of the case and supplementary materials via Harvard Business Publishing, please access the following links:
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