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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.

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  • 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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    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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    16. Jingjing Long & Cuiqing Jiang & Stanko Dimitrov & Zhao Wang, 2022. "Clues from networks: quantifying relational risk for credit risk evaluation of SMEs," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-41, December.
    17. 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.
    18. Qilun Li & Zhaoyi Xu & Xiaoqin Shen & Jiacheng Zhong, 2022. "Predicting Business Risks of Commercial Banks Based on BP-GA Optimized Model," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1423-1441, April.
    19. Mao, Wenxin & Wang, Wenping & Sun, Huifang & Yao, Peiyi & Wang, Xiaolei & Luo, Dang, 2021. "Urban industrial transformation patterns under natural resource dependence: A rule mining technique," Energy Policy, Elsevier, vol. 156(C).
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    22. 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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