摘要翻译：
本文介绍了一种基于决策树学习器的非寿险公司财务状况的预警系统，用于对非寿险公司的财务状况进行强、中、弱或破产分类。在本研究中，我们使用标准的10倍交叉验证、分离训练和测试数据集以及分离测试集进行了多个实验，结果表明所提出的模型能够取得良好的效果。结果表明，该方法是有效的，能够准确地对偿付能力状况进行分类。
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英文标题：
《Using Decision Tree Learner to Classify Solvency Position for Thai
  Non-life Insurance Companies》
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作者：
Phaiboon Jhongpita, Sukree Sinthupinyo and Thitivadee Chaiyawat
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最新提交年份：
2012
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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英文摘要：
  This paper introduces a Decision Tree Learner as an early warning system for classification of the non-life insurance companies according to their financial solid as strong, moderate, weak, or insolvency. In this study, we ran several experiments to show that the proposed model can achieve a good result using standard 10 fold crossvalidation, split train and test data set, and separated test set. The results show that the method is effective and can accurately classify the solvency position.
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PDF链接：
https://arxiv.org/pdf/1203.3031