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Determinants and Prediction Model for Rural Bank Sustainability in Indonesia Post-COVID-19

Author

Listed:
  • Devy Mawarnie Puspitasari

    (Department of Economics and Business, Universitas Mercu Buana, Jakarta 11650, Indonesia)

  • Jacky Chin

    (Department of Engineering, Universitas Mercu Buana, Jakarta 11650, Indonesia)

  • Sunita Dasman

    (Department of Economics and Business, Universitas Pelita Bangsa, Bekasi 17530, Indonesia)

Abstract

This study investigates the key risk factors influencing the sustainability of rural banks in Indonesia following the COVID-19 pandemic. It develops a predictive model of rural bank sustainability using logistic regression analysis. The analysis identifies seven statistically significant financial indicators, and among the three models proposed, Model 3 demonstrates the highest predictive accuracy, both in-sample and out-of-sample. Robustness tests confirm the reliability of this model. The findings highlight the importance for rural banks to improve their financial management, particularly in liquidity, credit expansion, and operational efficiency, to achieve long-term sustainability in a post-crisis economic landscape.

Suggested Citation

  • Devy Mawarnie Puspitasari & Jacky Chin & Sunita Dasman, 2025. "Determinants and Prediction Model for Rural Bank Sustainability in Indonesia Post-COVID-19," Sustainability, MDPI, vol. 17(16), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7207-:d:1720979
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    References listed on IDEAS

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    4. Fanlong Zeng & Jintao Wang & Chaoyan Zeng, 2025. "An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior," PLOS ONE, Public Library of Science, vol. 20(3), pages 1-25, March.
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