Banking credit worthiness: Evaluating the complex relationships

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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

28 Citations (Scopus)

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.

Original languageEnglish
Peer-reviewed scientific journalOmega : The International Journal of Management Science
Volume83
Issue numberMarch
Pages (from-to)26-38
Number of pages13
ISSN0305-0483
DOIs
Publication statusPublished - 01.03.2019
MoE publication typeA1 Journal article - refereed

Keywords

  • 512 Business and Management
  • Or in banking
  • Credit risk
  • Fuzzy rough-set
  • Fuzzy C-means
  • Farmers
  • China

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