期刊
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
卷 83, 期 -, 页码 26-38出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.omega.2018.02.001
关键词
Or in banking; Credit risk; Fuzzy rough-set; Fuzzy C-means; Farmers; China
资金
- National Natural Science Foundation of China [71772032, 71503199, 71502026, 714720311]
- Key Projects of National Natural Science Foundation of China [71731003]
- Credit Risks Evaluation and Loan Pricing for Petty Loan Funded for the Head Office of Post Savings Bank of China [2009-07]
- Youth Talent Cultivation Program of Northwest AF University [Z109021717]
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. (C) 2018 Elsevier Ltd. All rights reserved.
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