Predicting Microfinance Credit Default: A Study of Nsoatreman Rural Bank, Ghana

Ernest Yeboah Boateng, Francis T. Oduro · 2018 · Journal of Advances in Mathematics and Computer Science

Summary. This study develops predictive models to identify which microfinance borrowers at a rural Ghanaian bank will default on loans. Using data from Nsoatreman Rural Bank, the researchers apply machine learning techniques to forecast credit default risk. The findings help rural financial institutions better assess borrower creditworthiness and manage lending decisions more effectively.

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Boateng, E. Y., & Oduro, F. T.. (2018). Predicting Microfinance Credit Default: A Study of Nsoatreman Rural Bank, Ghana. Journal of Advances in Mathematics and Computer Science. https://doi.org/10.9734/jamcs/2018/33569

@article{boateng-2018-predicting-microfinance-credit-default-study,
  title   = {Predicting Microfinance Credit Default: A Study of Nsoatreman Rural Bank, Ghana},
  author  = {Ernest Yeboah Boateng and Francis T. Oduro},
  journal = {Journal of Advances in Mathematics and Computer Science},
  year    = {2018},
  doi     = {10.9734/jamcs/2018/33569},
  url     = {https://doi.org/10.9734/jamcs/2018/33569}
}

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TI  - Predicting Microfinance Credit Default: A Study of Nsoatreman Rural Bank, Ghana
AU  - Ernest Yeboah Boateng
AU  - Francis T. Oduro
JO  - Journal of Advances in Mathematics and Computer Science
PY  - 2018
DO  - 10.9734/jamcs/2018/33569
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Details

DOI: 10.9734/jamcs/2018/33569
Countries: Ghana
Regions: Africa
Categories: funding, rural-data-and-definitions
Added: 2026-04-28