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Banking credit worthiness: Evaluating the complex relationships

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  • Bai, Chunguang
  • Shi, Baofeng
  • Liu, Feng
  • Sarkis, Joseph

Abstract

In developing economies agriculture and farming play crucial roles for economic sustainable development. Farmer credit risk evaluation is an important issue when determining financial support to farmers, improving agricultural supply chain performance, and ensuring profitability of financial institutions. Credit risk evaluation, or creditworthiness, is not a trivial exercise due to various complexities. Honoring complexity is necessary to effectively evaluate and predict farmer creditworthiness. A methodology using fuzzy rough-set theory and fuzzy C-means clustering is used to evaluate and investigate the complex relationships between farmer characteristics, competitive environmental factors, and farmer credit level. The methodology is detailed using actual bank data from 2044 farmers within China. This empirical methodology generates decision rules that provide insight to more complex relationships than can be found through standard econometric multivariate approaches. A rule-based methodological outcome can be used to predict the creditworthiness of farmers and to aid in agricultural loan decision making. Prediction accuracy of the rule-base was 81.16%. A central finding is that education and skills related characteristics are important for determining farmer credit-worthiness. Other implications are presented along with study limitations and future research directions.

Suggested Citation

  • Bai, Chunguang & Shi, Baofeng & Liu, Feng & Sarkis, Joseph, 2019. "Banking credit worthiness: Evaluating the complex relationships," Omega, Elsevier, vol. 83(C), pages 26-38.
    • Handle: RePEc:eee:jomega:v:83:y:2019:i:c:p:26-38
    DOI: 10.1016/j.omega.2018.02.001
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    4. Salihu, Armend & Shehu, Visar, 2020. "A Review of Algorithms for Credit Risk Analysis," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2020), Virtual Conference, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020, pages 134-146, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    5. Shi, Baofeng & Zhao, Xue & Wu, Bi & Dong, Yizhe, 2019. "Credit rating and microfinance lending decisions based on loss given default (LGD)," Finance Research Letters, Elsevier, vol. 30(C), pages 124-129.
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    8. Yuehua Xia & Honggen Long & Zhi Li & Jiasen Wang, 2022. "Farmers’ Credit Risk Assessment Based on Sustainable Supply Chain Finance for Green Agriculture," Sustainability, MDPI, vol. 14(19), pages 1-20, October.
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    14. Shi, Baofeng & Chi, Guotai & Li, Weiping, 2020. "Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach," Economic Modelling, Elsevier, vol. 85(C), pages 420-428.
    15. Julio Cezar Soares Silva & Diogo Ferreira de Lima Silva & Luciano Ferreira & Adiel Teixeira de Almeida-Filho, 2022. "A dominance-based rough set approach applied to evaluate the credit risk of sovereign bonds," 4OR, Springer, vol. 20(1), pages 139-164, March.
    16. Sun, Yue & Chai, Nana & Dong, Yizhe & Shi, Baofeng, 2022. "Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1158-1172.
    17. Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
    18. Pranith Kumar Roy & Krishnendu Shaw, 2022. "Developing a multi-criteria sustainable credit score system using fuzzy BWM and fuzzy TOPSIS," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(4), pages 5368-5399, April.
    19. Sanjiv Das & Xin Huang & Soji Adeshina & Patrick Yang & Leonardo Bachega, 2023. "Credit Risk Modeling with Graph Machine Learning," INFORMS Joural on Data Science, INFORMS, vol. 2(2), pages 197-217, October.
    20. Nana Chai & Baofeng Shi & Bin Meng & Yizhe Dong, 2023. "Default Feature Selection in Credit Risk Modeling: Evidence From Chinese Small Enterprises," SAGE Open, , vol. 13(2), pages 21582440231, April.
    21. Mohammad S. Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib, 2022. "Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3713-3729, July.
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    25. Iulia Cristina Iuga & Larisa-Loredana Dragolea, 2021. "Well-Being Impact on Banking Systems," JRFM, MDPI, vol. 14(3), pages 1-22, March.

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