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Spatial dependence in microfinance credit default

Author

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  • Medina-Olivares, Victor
  • Calabrese, Raffaella
  • Dong, Yizhe
  • Shi, Baofeng

Abstract

Credit scoring model development is very important for the lending decisions of financial institutions. The creditworthiness of borrowers is evaluated by assessing their hard and soft information. However, microfinance borrowers are very sensitive to a local economic downturn and extreme (weather or climate) events. Therefore, this paper is devoted to extending the standard credit scoring models by taking into account the spatial dependence in credit risk. We estimate a credit scoring model with spatial random effects using the distance matrix based on the borrowers’ locations. We find that including the spatial random effects improves the ability to predict defaults and non-defaults of both individual and group loans. Furthermore, we find that several loan characteristics and demographic information are important determinants of individual loan default but not group loans. Our study provides valuable insights for professionals and academics in credit scoring for microfinance and rural finance.

Suggested Citation

  • Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:3:p:1071-1085
    DOI: 10.1016/j.ijforecast.2021.05.009
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    Cited by:

    1. Chai, Nana & Shi, Baofeng & Hua, Yiting, 2023. "Loss given default or default status: Which is better to determine farmers’ credit ratings?," Finance Research Letters, Elsevier, vol. 53(C).
    2. Pascal Kundig & Fabio Sigrist, 2024. "A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios," Papers 2410.02846, arXiv.org.
    3. Francis Lwesya & Adam Beni Swebe Mwakalobo, 2023. "Frontiers in microfinance research for small and medium enterprises (SMEs) and microfinance institutions (MFIs): a bibliometric analysis," Future Business Journal, Springer, vol. 9(1), pages 1-18, December.
    4. Victor Medina-Olivares & Finn Lindgren & Raffaella Calabrese & Jonathan Crook, 2023. "Joint model for longitudinal and spatio-temporal survival data," Papers 2311.04008, arXiv.org.

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