The recent economic downturn in the agricultural sector that started in 2013 has caused some concerns for farmers’ repayment capacity, which raises the need for precise prediction of financial stress in the agricultural sector. Machine learning has been shown to improve predictions with large financial data, however, its application remains limited in the agricultural sector. In this study, we approximate financial stress by agricultural loan delinquency, and predict it by employing a logistic regression and several machine learning methods. The main datasets include the Call Reports and Summary of Deposits from the Federal Deposit Insurance Corporation (FDIC). Our results show that ensemble learning methods have the best performance in prediction accuracy, with improvement of 26 percentage points at most and that the Naïve Bayes classifier is the best method to maintain the lowest cost from false predictions when the failure of identifying potentially high-risk loans is very costly. From the perspective of banks, while there are important benefits to using machine learning, the bank-level costs are also important considerations that may lead to different choices of machine learning methods.

中文翻译：

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使用机器学习方法预测农业贷款拖欠

始于 2013 年的农业部门近期经济下滑引发了对农民还款能力的一些担忧，这就提出了对农业部门财务压力进行精确预测的必要性。机器学习已被证明可以改善对大量金融数据的预测，但是，其在农业领域的应用仍然有限。在这项研究中，我们通过农业贷款拖欠来估计财务压力，并通过使用逻辑回归和多种机器学习方法对其进行预测。主要数据集包括来自联邦存款保险公司 (FDIC) 的调用报告和存款摘要。我们的结果表明，集成学习方法在预测准确性方面具有最佳性能，最多提高 26 个百分点，并且当识别潜在高风险贷款的失败成本非常高时，朴素贝叶斯分类器是保持错误预测成本最低的最佳方法。从银行的角度来看，虽然使用机器学习有重要的好处，但银行层面的成本也是重要的考虑因素，可能会导致机器学习方法的不同选择。