Rural Micro Credit Assessment using Machine Learning in a Peruvian microfinance institution

Henry Iván Condori-Alejo, Miguel Romilio Aceituno-Rojo, Guina Sotomayor Alzamora · 2021 · Procedia Computer Science

Summary. A machine learning model using artificial neural networks improves microcredit assessment for rural borrowers in Peru. The model achieved 93.72% accuracy in predicting loan defaults, outperforming the microfinance institution's traditional advisor-based method by 16.91 percentage points. This decision-support tool helps reduce credit risk by analyzing key financial and rural variables when evaluating loan applications from poor rural populations.

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@article{condori-alejo-2021-rural-micro-credit-assessment-using,
  title   = {Rural Micro Credit Assessment using Machine Learning in a Peruvian microfinance institution},
  author  = {Henry Iván Condori-Alejo and Miguel Romilio Aceituno-Rojo and Guina Sotomayor Alzamora},
  journal = {Procedia Computer Science},
  year    = {2021},
  doi     = {10.1016/j.procs.2021.04.117},
  url     = {https://doi.org/10.1016/j.procs.2021.04.117}
}

TY  - JOUR
TI  - Rural Micro Credit Assessment using Machine Learning in a Peruvian microfinance institution
AU  - Henry Iván Condori-Alejo
AU  - Miguel Romilio Aceituno-Rojo
AU  - Guina Sotomayor Alzamora
JO  - Procedia Computer Science
PY  - 2021
DO  - 10.1016/j.procs.2021.04.117
UR  - https://doi.org/10.1016/j.procs.2021.04.117
ER  - 

Details

DOI

10.1016/j.procs.2021.04.117

Countries

Peru

Regions

South America

Categories

funding, broadband-and-digital

Added

2026-04-28