Daung Capital: Managing the credit risk challenges of microfinance in Myanmar
In February 2021, political turmoil had engulfed Myanmar following a military coup under which elected leader Aung San Suu Kyi was jailed after her party won a landslide victory in the elections. CEO and co-founder of Daung Capital (Daung), a microfinance institution (MFI) in Myanmar, Leon Qiu recal...
Saved in:
| Main Authors: | , , |
|---|---|
| 格式: | text |
| 語言: | English |
| 出版: |
Institutional Knowledge at Singapore Management University
2022
|
| 主題: | |
| 在線閱讀: | https://ink.library.smu.edu.sg/cases_coll_all/306 https://smu.sharepoint.com/sites/admin/CMP/cases/SMU-22-BATCH%20%5BPDF-Pic%5D/SMU-22-0006%20%5BDaung%20Capital%5D/SMU-22-0006%20%5BDaung%20Capital%5D.pdf?CT=1650440185067&OR=ItemsView |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Singapore Management University |
| 語言: | English |
| 總結: | In February 2021, political turmoil had engulfed Myanmar following a military coup under which elected leader Aung San Suu Kyi was jailed after her party won a landslide victory in the elections. CEO and co-founder of Daung Capital (Daung), a microfinance institution (MFI) in Myanmar, Leon Qiu recalled the years before the coup, when inclusive finance businesses like his had just begun to flourish.
Daung had offered a variety of loan products in Myanmar since its inception in 2017. Its products included short-term loans for purchasing motorbikes, short-term loans to salaried workers for purchase of everyday utility items, and educational loans to students from poorer households. In 2019, Daung had launched a new loan product for farmers. Unlike other loan products, the loan scheme for farmers did not entail regular repayments. Instead, the farmers were expected to repay the loan as a lump sum after the harvest.
Careful credit risk assessment of the target customer base was a key criterion in designing a loan product for farmers. Ethical obligations and decision-making were important considerations as well. Qiu pondered over the various constraints around which the farmer loan product had been constructed and how it could be improved. How could Qiu and his team assess the credit risks associated with the farmer loan product? What strategies could they implement to control the credit risks associated with farmer loans? Could they use machine-learning algorithms to assess the credit risks?
The case illustrates (1) the various challenges faced by an MFI in assessing credit risk of agricultural borrowers (mainly farmers) (2) the dynamics of the MFI sector in Myanmar (3) credit risks presented by rural lenders. The case can be used in undergraduate, postgraduate and executive education classes to teach concepts on credit risk management in microfinance for emerging markets. |
|---|